diff --git a/docs/sections/customizing.md b/docs/sections/customizing.md
index 323ffb5..b4b5bba 100644
--- a/docs/sections/customizing.md
+++ b/docs/sections/customizing.md
@@ -410,10 +410,10 @@
 
   private:
     /* Maximum number of individual operations that can be enlisted. */
-    static constexpr int _ms_maxOpCnt = 5;
+    static constexpr int ms_maxOpCnt = 5;
 
     /* A mutable op resolver instance. */
-    tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+    tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
   };
 } /* namespace app */
 } /* namespace arm */
diff --git a/source/application/hal/hal.c b/source/application/hal/hal.c
index dbf94ba..9c2ce32 100644
--- a/source/application/hal/hal.c
+++ b/source/application/hal/hal.c
@@ -32,7 +32,7 @@
  * @brief   Initialises the Arm Ethos-U55 NPU
  * @return  0 if successful, error code otherwise
  **/
-static int _arm_npu_init(void);
+static int arm_npu_init(void);
 
 #endif /* ARM_NPU */
 
@@ -54,7 +54,7 @@
 /**
  * @brief  Local helper function to clean the slate for current platform.
  **/
-static void _hal_platform_clear(hal_platform* platform)
+static void hal_platform_clear(hal_platform* platform)
 {
     assert(platform);
     platform->inited = 0;
@@ -64,7 +64,7 @@
 {
     int state;
     assert(platform && platform->platform_init);
-    _hal_platform_clear(platform);
+    hal_platform_clear(platform);
 
     /* Initialise platform */
     if (0 != (state = platform->platform_init())) {
@@ -94,7 +94,7 @@
 #if defined(ARM_NPU)
 
     /* If Arm Ethos-U55 NPU is to be used, we initialise it here */
-    if (0 != (state = _arm_npu_init())) {
+    if (0 != (state = arm_npu_init())) {
         return state;
     }
 
@@ -120,7 +120,7 @@
     data_acq_channel_release(platform->data_acq);
     data_psn_system_release(platform->data_psn);
 
-    _hal_platform_clear(platform);
+    hal_platform_clear(platform);
     info("releasing platform %s\n", platform->plat_name);
     platform->platform_release();
 }
@@ -130,7 +130,7 @@
  * @brief   Defines the Ethos-U interrupt handler: just a wrapper around the default
  *          implementation.
  **/
-static void _arm_npu_irq_handler(void)
+static void arm_npu_irq_handler(void)
 {
     /* Call the default interrupt handler from the NPU driver */
     ethosu_irq_handler();
@@ -139,19 +139,19 @@
 /**
  * @brief  Initialises the NPU IRQ
  **/
-static void _arm_npu_irq_init(void)
+static void arm_npu_irq_init(void)
 {
     const IRQn_Type ethosu_irqnum = (IRQn_Type)EthosU_IRQn;
 
     /* Register the EthosU IRQ handler in our vector table.
      * Note, this handler comes from the EthosU driver */
-    NVIC_SetVector(ethosu_irqnum, (uint32_t)_arm_npu_irq_handler);
+    NVIC_SetVector(ethosu_irqnum, (uint32_t)arm_npu_irq_handler);
 
     /* Enable the IRQ */
     NVIC_EnableIRQ(ethosu_irqnum);
 
     debug("EthosU IRQ#: %u, Handler: 0x%p\n",
-            ethosu_irqnum, _arm_npu_irq_handler);
+            ethosu_irqnum, arm_npu_irq_handler);
 }
 
 static int _arm_npu_timing_adapter_init(void)
@@ -213,7 +213,7 @@
     return 0;
 }
 
-static int _arm_npu_init(void)
+static int arm_npu_init(void)
 {
     int err = 0;
 
@@ -224,7 +224,7 @@
     }
 
     /* Initialise the IRQ */
-    _arm_npu_irq_init();
+    arm_npu_irq_init();
 
     /* Initialise Ethos-U55 device */
     const void * ethosu_base_address = (void *)(SEC_ETHOS_U55_BASE);
diff --git a/source/application/main/Classifier.cc b/source/application/main/Classifier.cc
index bc2c378..9a47f3d 100644
--- a/source/application/main/Classifier.cc
+++ b/source/application/main/Classifier.cc
@@ -28,10 +28,46 @@
 namespace app {
 
     template<typename T>
-    bool Classifier::_GetTopNResults(TfLiteTensor* tensor,
-                         std::vector<ClassificationResult>& vecResults,
-                         uint32_t topNCount,
-                         const std::vector <std::string>& labels)
+    void SetVectorResults(std::set<std::pair<T, uint32_t>>& topNSet,
+                          std::vector<ClassificationResult>& vecResults,
+                          TfLiteTensor* tensor,
+                          const std::vector <std::string>& labels) {
+
+        /* For getting the floating point values, we need quantization parameters. */
+        QuantParams quantParams = GetTensorQuantParams(tensor);
+
+        /* Reset the iterator to the largest element - use reverse iterator. */
+        auto topNIter = topNSet.rbegin();
+        for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
+            T score = topNIter->first;
+            vecResults[i].m_normalisedVal = quantParams.scale * (score - quantParams.offset);
+            vecResults[i].m_label = labels[topNIter->second];
+            vecResults[i].m_labelIdx = topNIter->second;
+        }
+
+    }
+
+    template<>
+    void SetVectorResults<float>(std::set<std::pair<float, uint32_t>>& topNSet,
+                          std::vector<ClassificationResult>& vecResults,
+                          TfLiteTensor* tensor,
+                          const std::vector <std::string>& labels) {
+        UNUSED(tensor);
+        /* Reset the iterator to the largest element - use reverse iterator. */
+        auto topNIter = topNSet.rbegin();
+        for (size_t i = 0; i < vecResults.size() && topNIter != topNSet.rend(); ++i, ++topNIter) {
+            vecResults[i].m_normalisedVal = topNIter->first;
+            vecResults[i].m_label = labels[topNIter->second];
+            vecResults[i].m_labelIdx = topNIter->second;
+        }
+
+    }
+
+    template<typename T>
+    bool Classifier::GetTopNResults(TfLiteTensor* tensor,
+                                    std::vector<ClassificationResult>& vecResults,
+                                    uint32_t topNCount,
+                                    const std::vector <std::string>& labels)
     {
         std::set<std::pair<T, uint32_t>> sortedSet;
 
@@ -59,82 +95,18 @@
         /* Final results' container. */
         vecResults = std::vector<ClassificationResult>(topNCount);
 
-        /* For getting the floating point values, we need quantization parameters. */
-        QuantParams quantParams = GetTensorQuantParams(tensor);
-
-        /* Reset the iterator to the largest element - use reverse iterator. */
-        auto setRevIter = sortedSet.rbegin();
-
-        /* Populate results
-         * Note: we could combine this loop with the loop above, but that
-         *       would, involve more multiplications and other operations.
-         **/
-        for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
-            double score = static_cast<int> (setRevIter->first);
-            vecResults[i].m_normalisedVal = quantParams.scale *
-                                         (score - quantParams.offset);
-            vecResults[i].m_label = labels[setRevIter->second];
-            vecResults[i].m_labelIdx = setRevIter->second;
-        }
+        SetVectorResults<T>(sortedSet, vecResults, tensor, labels);
 
         return true;
     }
 
-    template<>
-    bool Classifier::_GetTopNResults<float>(TfLiteTensor* tensor,
-                                     std::vector<ClassificationResult>& vecResults,
-                                     uint32_t topNCount,
-                                     const std::vector <std::string>& labels)
-    {
-        std::set<std::pair<float, uint32_t>> sortedSet;
+    template bool  Classifier::GetTopNResults<uint8_t>(TfLiteTensor* tensor,
+                                                       std::vector<ClassificationResult>& vecResults,
+                                                       uint32_t topNCount, const std::vector <std::string>& labels);
 
-        /* NOTE: inputVec's size verification against labels should be
-         *       checked by the calling/public function. */
-        float* tensorData = tflite::GetTensorData<float>(tensor);
-
-        /* Set initial elements. */
-        for (uint32_t i = 0; i < topNCount; ++i) {
-            sortedSet.insert({tensorData[i], i});
-        }
-
-        /* Initialise iterator. */
-        auto setFwdIter = sortedSet.begin();
-
-        /* Scan through the rest of elements with compare operations. */
-        for (uint32_t i = topNCount; i < labels.size(); ++i) {
-            if (setFwdIter->first < tensorData[i]) {
-                sortedSet.erase(*setFwdIter);
-                sortedSet.insert({tensorData[i], i});
-                setFwdIter = sortedSet.begin();
-            }
-        }
-
-        /* Final results' container. */
-        vecResults = std::vector<ClassificationResult>(topNCount);
-
-        /* Reset the iterator to the largest element - use reverse iterator. */
-        auto setRevIter = sortedSet.rbegin();
-
-        /* Populate results
-         * Note: we could combine this loop with the loop above, but that
-         *       would, involve more multiplications and other operations.
-         **/
-        for (size_t i = 0; i < vecResults.size(); ++i, ++setRevIter) {
-            vecResults[i].m_normalisedVal = setRevIter->first;
-            vecResults[i].m_label = labels[setRevIter->second];
-            vecResults[i].m_labelIdx = setRevIter->second;
-        }
-
-        return true;
-    }
-
-    template bool  Classifier::_GetTopNResults<uint8_t>(TfLiteTensor* tensor,
-                                           std::vector<ClassificationResult>& vecResults,
-                                           uint32_t topNCount, const std::vector <std::string>& labels);
-
-    template bool  Classifier::_GetTopNResults<int8_t>(TfLiteTensor* tensor,
-                                          std::vector<ClassificationResult>& vecResults,
-                                          uint32_t topNCount, const std::vector <std::string>& labels);
+    template bool  Classifier::GetTopNResults<int8_t>(TfLiteTensor* tensor,
+                                                      std::vector<ClassificationResult>& vecResults,
+                                                      uint32_t topNCount, const std::vector <std::string>& labels);
 
     bool  Classifier::GetClassificationResults(
         TfLiteTensor* outputTensor,
@@ -158,6 +130,9 @@
         } else if (totalOutputSize != labels.size()) {
             printf_err("Output size doesn't match the labels' size\n");
             return false;
+        } else if (topNCount == 0) {
+            printf_err("Top N results cannot be zero\n");
+            return false;
         }
 
         bool resultState;
@@ -166,13 +141,13 @@
         /* Get the top N results. */
         switch (outputTensor->type) {
             case kTfLiteUInt8:
-                resultState = _GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
+                resultState = GetTopNResults<uint8_t>(outputTensor, vecResults, topNCount, labels);
                 break;
             case kTfLiteInt8:
-                resultState = _GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
+                resultState = GetTopNResults<int8_t>(outputTensor, vecResults, topNCount, labels);
                 break;
             case kTfLiteFloat32:
-                resultState = _GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
+                resultState = GetTopNResults<float>(outputTensor, vecResults, topNCount, labels);
                 break;
             default:
                 printf_err("Tensor type %s not supported by classifier\n", TfLiteTypeGetName(outputTensor->type));
@@ -180,7 +155,7 @@
         }
 
         if (!resultState) {
-            printf_err("Failed to get sorted set\n");
+            printf_err("Failed to get top N results set\n");
             return false;
         }
 
diff --git a/source/application/main/Mfcc.cc b/source/application/main/Mfcc.cc
index bf16159..9ddcb5d 100644
--- a/source/application/main/Mfcc.cc
+++ b/source/application/main/Mfcc.cc
@@ -44,7 +44,7 @@
                         m_useHtkMethod(useHtkMethod)
     {}
 
-    std::string MfccParams::Str()
+    std::string MfccParams::Str() const
     {
         char strC[1024];
         snprintf(strC, sizeof(strC) - 1, "\n   \
@@ -74,7 +74,7 @@
                                 this->_m_params.m_numFbankBins, 0.0);
 
         this->_m_windowFunc = std::vector<float>(this->_m_params.m_frameLen);
-        const float multiplier = 2 * M_PI / this->_m_params.m_frameLen;
+        const auto multiplier = static_cast<float>(2 * M_PI / this->_m_params.m_frameLen);
 
         /* Create window function. */
         for (size_t i = 0; i < this->_m_params.m_frameLen; i++) {
@@ -88,7 +88,7 @@
 
     void MFCC::Init()
     {
-        this->_InitMelFilterBank();
+        this->InitMelFilterBank();
     }
 
     float MFCC::MelScale(const float freq, const bool useHTKMethod)
@@ -126,8 +126,8 @@
     bool MFCC::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&               filterBankFilterFirst,
+            std::vector<uint32_t>&               filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -140,11 +140,12 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
+            auto end = melFilterBank[bin].end();
             float melEnergy = FLT_MIN;  /* Avoid log of zero at later stages */
-            int32_t firstIndex = filterBankFilterFirst[bin];
-            int32_t lastIndex = filterBankFilterLast[bin];
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int i = firstIndex; i <= lastIndex; i++) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; i++) {
                 float energyRep = math::MathUtils::SqrtF32(fftVec[i]);
                 melEnergy += (*filterBankIter++ * energyRep);
             }
@@ -157,14 +158,14 @@
 
     void MFCC::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
     {
-        for (size_t bin = 0; bin < melEnergies.size(); ++bin) {
-            melEnergies[bin] = logf(melEnergies[bin]);
+        for (float& melEnergy : melEnergies) {
+            melEnergy = logf(melEnergy);
         }
     }
 
-    void MFCC::_ConvertToPowerSpectrum()
+    void MFCC::ConvertToPowerSpectrum()
     {
-        const uint32_t halfDim = this->_m_params.m_frameLenPadded / 2;
+        const uint32_t halfDim = this->_m_buffer.size() / 2;
 
         /* Handle this special case. */
         float firstEnergy = this->_m_buffer[0] * this->_m_buffer[0];
@@ -193,7 +194,7 @@
         for (int32_t k = 0, m = 0; k < coefficientCount; k++, m += inputLength) {
             for (int32_t n = 0; n < inputLength; n++) {
                 dctMatix[m+n] = normalizer *
-                    math::MathUtils::CosineF32((n + 0.5) * angle);
+                    math::MathUtils::CosineF32((n + 0.5f) * angle);
             }
             angle += angleIncr;
         }
@@ -214,10 +215,10 @@
         return 1.f;
     }
 
-    void MFCC::_InitMelFilterBank()
+    void MFCC::InitMelFilterBank()
     {
-        if (!this->_IsMelFilterBankInited()) {
-            this->_m_melFilterBank = this->_CreateMelFilterBank();
+        if (!this->IsMelFilterBankInited()) {
+            this->_m_melFilterBank = this->CreateMelFilterBank();
             this->_m_dctMatrix = this->CreateDCTMatrix(
                                     this->_m_params.m_numFbankBins,
                                     this->_m_params.m_numMfccFeatures);
@@ -225,17 +226,17 @@
         }
     }
 
-    bool MFCC::_IsMelFilterBankInited()
+    bool MFCC::IsMelFilterBankInited() const
     {
         return this->_m_filterBankInitialised;
     }
 
-    void MFCC::_MfccComputePreFeature(const std::vector<int16_t>& audioData)
+    void MFCC::MfccComputePreFeature(const std::vector<int16_t>& audioData)
     {
-        this->_InitMelFilterBank();
+        this->InitMelFilterBank();
 
         /* TensorFlow way of normalizing .wav data to (-1, 1). */
-        constexpr float normaliser = 1.0/(1<<15);
+        constexpr float normaliser = 1.0/(1u<<15u);
         for (size_t i = 0; i < this->_m_params.m_frameLen; i++) {
             this->_m_frame[i] = static_cast<float>(audioData[i]) * normaliser;
         }
@@ -252,7 +253,7 @@
         math::MathUtils::FftF32(this->_m_frame, this->_m_buffer, this->_m_fftInstance);
 
         /* Convert to power spectrum. */
-        this->_ConvertToPowerSpectrum();
+        this->ConvertToPowerSpectrum();
 
         /* Apply mel filterbanks. */
         if (!this->ApplyMelFilterBank(this->_m_buffer,
@@ -269,7 +270,7 @@
 
     std::vector<float> MFCC::MfccCompute(const std::vector<int16_t>& audioData)
     {
-        this->_MfccComputePreFeature(audioData);
+        this->MfccComputePreFeature(audioData);
 
         std::vector<float> mfccOut(this->_m_params.m_numMfccFeatures);
 
@@ -288,7 +289,7 @@
         return mfccOut;
     }
 
-    std::vector<std::vector<float>> MFCC::_CreateMelFilterBank()
+    std::vector<std::vector<float>> MFCC::CreateMelFilterBank()
     {
         size_t numFftBins = this->_m_params.m_frameLenPadded / 2;
         float fftBinWidth = static_cast<float>(this->_m_params.m_samplingFreq) / this->_m_params.m_frameLenPadded;
@@ -303,17 +304,18 @@
         std::vector<std::vector<float>> melFilterBank(
                                             this->_m_params.m_numFbankBins);
         this->_m_filterBankFilterFirst =
-                        std::vector<int32_t>(this->_m_params.m_numFbankBins);
+                        std::vector<uint32_t>(this->_m_params.m_numFbankBins);
         this->_m_filterBankFilterLast =
-                        std::vector<int32_t>(this->_m_params.m_numFbankBins);
+                        std::vector<uint32_t>(this->_m_params.m_numFbankBins);
 
         for (size_t bin = 0; bin < this->_m_params.m_numFbankBins; bin++) {
             float leftMel = melLowFreq + bin * melFreqDelta;
             float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
             float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
 
-            int32_t firstIndex = -1;
-            int32_t lastIndex = -1;
+            uint32_t firstIndex = 0;
+            uint32_t lastIndex = 0;
+            bool firstIndexFound = false;
             const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->_m_params.m_useHtkMethod);
 
             for (size_t i = 0; i < numFftBins; i++) {
@@ -330,8 +332,9 @@
                     }
 
                     thisBin[i] = weight * normaliser;
-                    if (firstIndex == -1) {
+                    if (!firstIndexFound) {
                         firstIndex = i;
+                        firstIndexFound = true;
                     }
                     lastIndex = i;
                 }
@@ -341,7 +344,7 @@
             this->_m_filterBankFilterLast[bin] = lastIndex;
 
             /* Copy the part we care about. */
-            for (int32_t i = firstIndex; i <= lastIndex; i++) {
+            for (uint32_t i = firstIndex; i <= lastIndex; i++) {
                 melFilterBank[bin].push_back(thisBin[i]);
             }
         }
diff --git a/source/application/main/PlatformMath.cc b/source/application/main/PlatformMath.cc
index a9f5049..9d18151 100644
--- a/source/application/main/PlatformMath.cc
+++ b/source/application/main/PlatformMath.cc
@@ -121,7 +121,7 @@
             float sumReal = 0, sumImag = 0;
 
             for (int t = 0; t < inputLength; t++) {
-                float angle = 2 * M_PI * t * k / inputLength;
+                auto angle = static_cast<float>(2 * M_PI * t * k / inputLength);
                 sumReal += input[t] * cosf(angle);
                 sumImag += -input[t] * sinf(angle);
             }
@@ -147,7 +147,7 @@
                      output.size());
 #else /* ARM_DSP_AVAILABLE */
         for (auto in = input.begin(), out = output.begin();
-                in != input.end(); ++in, ++out) {
+             in != input.end() && out != output.end(); ++in, ++out) {
             *out = logf(*in);
         }
 #endif /* ARM_DSP_AVAILABLE */
diff --git a/source/application/main/Profiler.cc b/source/application/main/Profiler.cc
index 0456ba4..10a828a 100644
--- a/source/application/main/Profiler.cc
+++ b/source/application/main/Profiler.cc
@@ -54,7 +54,7 @@
             this->_m_tstampEnd = this->_m_pPlatform->timer->stop_profiling();
             this->_m_started = false;
 
-            this->_AddProfilingUnit(this->_m_tstampSt, this->_m_tstampEnd, this->_m_name);
+            this->AddProfilingUnit(this->_m_tstampSt, this->_m_tstampEnd, this->_m_name);
 
             return true;
         }
@@ -238,8 +238,8 @@
         this->_m_name = std::string(str);
     }
 
-    void Profiler::_AddProfilingUnit(time_counter start, time_counter end,
-                                     const std::string& name)
+    void Profiler::AddProfilingUnit(time_counter start, time_counter end,
+                                    const std::string& name)
     {
         platform_timer * timer = this->_m_pPlatform->timer;
 
diff --git a/source/application/main/include/Classifier.hpp b/source/application/main/include/Classifier.hpp
index 510e6f9..3ee3148 100644
--- a/source/application/main/include/Classifier.hpp
+++ b/source/application/main/include/Classifier.hpp
@@ -62,7 +62,7 @@
          * @return      true if successful, false otherwise.
          **/
         template<typename T>
-        bool _GetTopNResults(TfLiteTensor* tensor,
+        bool GetTopNResults(TfLiteTensor* tensor,
                             std::vector<ClassificationResult>& vecResults,
                             uint32_t topNCount,
                             const std::vector <std::string>& labels);
diff --git a/source/application/main/include/DataStructures.hpp b/source/application/main/include/DataStructures.hpp
index 5cc8b5e..2f267c0 100644
--- a/source/application/main/include/DataStructures.hpp
+++ b/source/application/main/include/DataStructures.hpp
@@ -47,15 +47,13 @@
          * @param[in] rows   Number of rows.
          * @param[in] cols   Number of columns.
          */
-        Array2d(unsigned rows, unsigned cols)
+        Array2d(unsigned rows, unsigned cols): _m_rows(rows), _m_cols(cols)
         {
             if (rows == 0 || cols == 0) {
                 printf_err("Array2d constructor has 0 size.\n");
                 _m_data = nullptr;
                 return;
             }
-            _m_rows = rows;
-            _m_cols = cols;
             _m_data = new T[rows * cols];
         }
 
diff --git a/source/application/main/include/Mfcc.hpp b/source/application/main/include/Mfcc.hpp
index 6379fab..dcafe62 100644
--- a/source/application/main/include/Mfcc.hpp
+++ b/source/application/main/include/Mfcc.hpp
@@ -52,7 +52,7 @@
         ~MfccParams() = default;
 
         /** @brief  String representation of parameters */
-        std::string Str();
+        std::string Str() const;
     };
 
     /**
@@ -100,7 +100,7 @@
                                         const float quantScale,
                                         const int quantOffset)
         {
-            this->_MfccComputePreFeature(audioData);
+            this->MfccComputePreFeature(audioData);
             float minVal = std::numeric_limits<T>::min();
             float maxVal = std::numeric_limits<T>::max();
 
@@ -154,7 +154,7 @@
          *              bank weights and adding them up to be placed into
          *              bins, according to the filter bank's first and last
          *              indices (pre-computed for each filter bank element
-         *              by _CreateMelFilterBank function).
+         *              by CreateMelFilterBank function).
          * @param[in]   fftVec                  Vector populated with FFT magnitudes.
          * @param[in]   melFilterBank           2D Vector with filter bank weights.
          * @param[in]   filterBankFilterFirst   Vector containing the first indices of filter bank
@@ -168,8 +168,8 @@
         virtual bool ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&               filterBankFilterFirst,
+            std::vector<uint32_t>&               filterBankFilterLast,
             std::vector<float>&                 melEnergies);
 
         /**
@@ -214,37 +214,37 @@
         std::vector<float>              _m_windowFunc;
         std::vector<std::vector<float>> _m_melFilterBank;
         std::vector<float>              _m_dctMatrix;
-        std::vector<int32_t>            _m_filterBankFilterFirst;
-        std::vector<int32_t>            _m_filterBankFilterLast;
+        std::vector<uint32_t>           _m_filterBankFilterFirst;
+        std::vector<uint32_t>           _m_filterBankFilterLast;
         bool                            _m_filterBankInitialised;
         arm::app::math::FftInstance     _m_fftInstance;
 
         /**
          * @brief       Initialises the filter banks and the DCT matrix. **/
-        void _InitMelFilterBank();
+        void InitMelFilterBank();
 
         /**
          * @brief       Signals whether the instance of MFCC has had its
          *              required buffers initialised.
          * @return      true if initialised, false otherwise.
          **/
-        bool _IsMelFilterBankInited();
+        bool IsMelFilterBankInited() const;
 
         /**
          * @brief       Create mel filter banks for MFCC calculation.
          * @return      2D vector of floats.
          **/
-        std::vector<std::vector<float>> _CreateMelFilterBank();
+        std::vector<std::vector<float>> CreateMelFilterBank();
 
         /**
          * @brief       Computes and populates internal memeber buffers used
          *              in MFCC feature calculation
          * @param[in]   audioData   1D vector of 16-bit audio data.
          */
-        void _MfccComputePreFeature(const std::vector<int16_t>& audioData);
+        void MfccComputePreFeature(const std::vector<int16_t>& audioData);
 
         /** @brief       Computes the magnitude from an interleaved complex array. */
-        void _ConvertToPowerSpectrum();
+        void ConvertToPowerSpectrum();
 
     };
 
diff --git a/source/application/main/include/Profiler.hpp b/source/application/main/include/Profiler.hpp
index d93b257..c5f77e7 100644
--- a/source/application/main/include/Profiler.hpp
+++ b/source/application/main/include/Profiler.hpp
@@ -125,8 +125,8 @@
          * @param[in]   name    Name for the profiling unit series to be
          *                      appended to.
          **/
-        void _AddProfilingUnit(time_counter start, time_counter end,
-                               const std::string& name);
+        void AddProfilingUnit(time_counter start, time_counter end,
+                              const std::string& name);
     };
 
 } /* namespace app */
diff --git a/source/application/tensorflow-lite-micro/Model.cc b/source/application/tensorflow-lite-micro/Model.cc
index 0775467..abf97b6 100644
--- a/source/application/tensorflow-lite-micro/Model.cc
+++ b/source/application/tensorflow-lite-micro/Model.cc
@@ -317,7 +317,7 @@
 }
 namespace arm {
 namespace app {
-    static uint8_t  _tensor_arena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+    static uint8_t  tensor_arena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
 } /* namespace app */
 } /* namespace arm */
 
@@ -328,5 +328,5 @@
 
 uint8_t *arm::app::Model::GetTensorArena()
 {
-    return _tensor_arena;
+    return tensor_arena;
 }
\ No newline at end of file
diff --git a/source/use_case/ad/include/AdMelSpectrogram.hpp b/source/use_case/ad/include/AdMelSpectrogram.hpp
index cf8a1d4..30a77c1 100644
--- a/source/use_case/ad/include/AdMelSpectrogram.hpp
+++ b/source/use_case/ad/include/AdMelSpectrogram.hpp
@@ -60,8 +60,8 @@
         virtual bool ApplyMelFilterBank(
                 std::vector<float>&                 fftVec,
                 std::vector<std::vector<float>>&    melFilterBank,
-                std::vector<int32_t>&               filterBankFilterFirst,
-                std::vector<int32_t>&               filterBankFilterLast,
+                std::vector<uint32_t>&               filterBankFilterFirst,
+                std::vector<uint32_t>&               filterBankFilterLast,
                 std::vector<float>&                 melEnergies) override;
 
         /**
diff --git a/source/use_case/ad/include/AdModel.hpp b/source/use_case/ad/include/AdModel.hpp
index 2d83455..bbdf91c 100644
--- a/source/use_case/ad/include/AdModel.hpp
+++ b/source/use_case/ad/include/AdModel.hpp
@@ -41,10 +41,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted */
-        static constexpr int _ms_maxOpCnt = 6;
+        static constexpr int ms_maxOpCnt = 6;
 
         /* A mutable op resolver instance */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/ad/include/MelSpectrogram.hpp b/source/use_case/ad/include/MelSpectrogram.hpp
index c1dd61e..22b5d29 100644
--- a/source/use_case/ad/include/MelSpectrogram.hpp
+++ b/source/use_case/ad/include/MelSpectrogram.hpp
@@ -49,7 +49,7 @@
         ~MelSpecParams() = default;
 
         /** @brief  String representation of parameters */
-        std::string Str();
+        std::string Str() const;
     };
 
     /**
@@ -76,7 +76,7 @@
          * @brief       Constructor
          * @param[in]   params - Mel Spectrogram parameters
         */
-        MelSpectrogram(const MelSpecParams& params);
+        explicit MelSpectrogram(const MelSpecParams& params);
 
         MelSpectrogram() = delete;
         ~MelSpectrogram() = default;
@@ -148,7 +148,7 @@
          *              bank weights and adding them up to be placed into
          *              bins, according to the filter bank's first and last
          *              indices (pre-computed for each filter bank element
-         *              by _CreateMelFilterBank function).
+         *              by CreateMelFilterBank function).
          * @param[in]   fftVec                  Vector populated with FFT magnitudes
          * @param[in]   melFilterBank           2D Vector with filter bank weights
          * @param[in]   filterBankFilterFirst   Vector containing the first indices of filter bank
@@ -162,8 +162,8 @@
         virtual bool ApplyMelFilterBank(
                 std::vector<float>&                 fftVec,
                 std::vector<std::vector<float>>&    melFilterBank,
-                std::vector<int32_t>&               filterBankFilterFirst,
-                std::vector<int32_t>&               filterBankFilterLast,
+                std::vector<uint32_t>&               filterBankFilterFirst,
+                std::vector<uint32_t>&               filterBankFilterLast,
                 std::vector<float>&                 melEnergies);
 
         /**
@@ -195,33 +195,33 @@
         std::vector<float>              _m_melEnergies;
         std::vector<float>              _m_windowFunc;
         std::vector<std::vector<float>> _m_melFilterBank;
-        std::vector<int32_t>            _m_filterBankFilterFirst;
-        std::vector<int32_t>            _m_filterBankFilterLast;
+        std::vector<uint32_t>            _m_filterBankFilterFirst;
+        std::vector<uint32_t>            _m_filterBankFilterLast;
         bool                            _m_filterBankInitialised;
         arm::app::math::FftInstance     _m_fftInstance;
 
         /**
          * @brief       Initialises the filter banks.
          **/
-        void _InitMelFilterBank();
+        void InitMelFilterBank();
 
         /**
          * @brief       Signals whether the instance of MelSpectrogram has had its
          *              required buffers initialised
          * @return      True if initialised, false otherwise
          **/
-        bool _IsMelFilterBankInited();
+        bool IsMelFilterBankInited() const;
 
         /**
          * @brief       Create mel filter banks for Mel Spectrogram calculation.
          * @return      2D vector of floats
          **/
-        std::vector<std::vector<float>> _CreateMelFilterBank();
+        std::vector<std::vector<float>> CreateMelFilterBank();
 
         /**
          * @brief       Computes the magnitude from an interleaved complex array
          **/
-        void _ConvertToPowerSpectrum();
+        void ConvertToPowerSpectrum();
 
     };
 
diff --git a/source/use_case/ad/src/AdMelSpectrogram.cc b/source/use_case/ad/src/AdMelSpectrogram.cc
index 183c05c..e070eb8 100644
--- a/source/use_case/ad/src/AdMelSpectrogram.cc
+++ b/source/use_case/ad/src/AdMelSpectrogram.cc
@@ -18,6 +18,8 @@
 
 #include "PlatformMath.hpp"
 
+#include <cfloat>
+
 namespace arm {
 namespace app {
 namespace audio {
@@ -25,8 +27,8 @@
     bool AdMelSpectrogram::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&               filterBankFilterFirst,
+            std::vector<uint32_t>&               filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -39,11 +41,12 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
-            float melEnergy = 1e-10; /* Avoid log of zero at later stages. */
-            const int32_t firstIndex = filterBankFilterFirst[bin];
-            const int32_t lastIndex = filterBankFilterLast[bin];
+            auto end = melFilterBank[bin].end();
+            float melEnergy = FLT_MIN; /* Avoid log of zero at later stages. */
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
                 melEnergy += (*filterBankIter++ * fftVec[i]);
             }
 
@@ -69,7 +72,7 @@
 
         /* Scale the log values. */
         for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
-             iterM != melEnergies.end(); ++iterM, ++iterL) {
+             iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
 
             *iterM = *iterL * multiplier;
         }
diff --git a/source/use_case/ad/src/MelSpectrogram.cc b/source/use_case/ad/src/MelSpectrogram.cc
index 86d57e6..372ebd8 100644
--- a/source/use_case/ad/src/MelSpectrogram.cc
+++ b/source/use_case/ad/src/MelSpectrogram.cc
@@ -42,7 +42,7 @@
             m_useHtkMethod(useHtkMethod)
     {}
 
-    std::string MelSpecParams::Str()
+    std::string MelSpecParams::Str() const
     {
         char strC[1024];
         snprintf(strC, sizeof(strC) - 1, "\n   \
@@ -71,7 +71,7 @@
                 this->_m_params.m_numFbankBins, 0.0);
 
         this->_m_windowFunc = std::vector<float>(this->_m_params.m_frameLen);
-        const float multiplier = 2 * M_PI / this->_m_params.m_frameLen;
+        const auto multiplier = static_cast<float>(2 * M_PI / this->_m_params.m_frameLen);
 
         /* Create window function. */
         for (size_t i = 0; i < this->_m_params.m_frameLen; ++i) {
@@ -85,7 +85,7 @@
 
     void MelSpectrogram::Init()
     {
-        this->_InitMelFilterBank();
+        this->InitMelFilterBank();
     }
 
     float MelSpectrogram::MelScale(const float freq, const bool useHTKMethod)
@@ -121,8 +121,8 @@
     bool MelSpectrogram::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&               filterBankFilterFirst,
+            std::vector<uint32_t>&               filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -135,11 +135,12 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
+            auto end = melFilterBank[bin].end();
             float melEnergy = FLT_MIN; /* Avoid log of zero at later stages */
-            int32_t firstIndex = filterBankFilterFirst[bin];
-            int32_t lastIndex = filterBankFilterLast[bin];
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
                 float energyRep = math::MathUtils::SqrtF32(fftVec[i]);
                 melEnergy += (*filterBankIter++ * energyRep);
             }
@@ -152,14 +153,14 @@
 
     void MelSpectrogram::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
     {
-        for (size_t bin = 0; bin < melEnergies.size(); ++bin) {
-            melEnergies[bin] = logf(melEnergies[bin]);
+        for (float& melEnergy : melEnergies) {
+            melEnergy = logf(melEnergy);
         }
     }
 
-    void MelSpectrogram::_ConvertToPowerSpectrum()
+    void MelSpectrogram::ConvertToPowerSpectrum()
     {
-        const uint32_t halfDim = this->_m_params.m_frameLenPadded / 2;
+        const uint32_t halfDim = this->_m_buffer.size() / 2;
 
         /* Handle this special case. */
         float firstEnergy = this->_m_buffer[0] * this->_m_buffer[0];
@@ -188,22 +189,22 @@
         return 1.f;
     }
 
-    void MelSpectrogram::_InitMelFilterBank()
+    void MelSpectrogram::InitMelFilterBank()
     {
-        if (!this->_IsMelFilterBankInited()) {
-            this->_m_melFilterBank = this->_CreateMelFilterBank();
+        if (!this->IsMelFilterBankInited()) {
+            this->_m_melFilterBank = this->CreateMelFilterBank();
             this->_m_filterBankInitialised = true;
         }
     }
 
-    bool MelSpectrogram::_IsMelFilterBankInited()
+    bool MelSpectrogram::IsMelFilterBankInited() const
     {
         return this->_m_filterBankInitialised;
     }
 
     std::vector<float> MelSpectrogram::ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean)
     {
-        this->_InitMelFilterBank();
+        this->InitMelFilterBank();
 
         /* TensorFlow way of normalizing .wav data to (-1, 1). */
         constexpr float normaliser = 1.0/(1<<15);
@@ -223,7 +224,7 @@
         math::MathUtils::FftF32(this->_m_frame, this->_m_buffer, this->_m_fftInstance);
 
         /* Convert to power spectrum. */
-        this->_ConvertToPowerSpectrum();
+        this->ConvertToPowerSpectrum();
 
         /* Apply mel filterbanks. */
         if (!this->ApplyMelFilterBank(this->_m_buffer,
@@ -245,7 +246,7 @@
         return this->_m_melEnergies;
     }
 
-    std::vector<std::vector<float>> MelSpectrogram::_CreateMelFilterBank()
+    std::vector<std::vector<float>> MelSpectrogram::CreateMelFilterBank()
     {
         size_t numFftBins = this->_m_params.m_frameLenPadded / 2;
         float fftBinWidth = static_cast<float>(this->_m_params.m_samplingFreq) / this->_m_params.m_frameLenPadded;
@@ -260,17 +261,18 @@
         std::vector<std::vector<float>> melFilterBank(
                 this->_m_params.m_numFbankBins);
         this->_m_filterBankFilterFirst =
-                std::vector<int32_t>(this->_m_params.m_numFbankBins);
+                std::vector<uint32_t>(this->_m_params.m_numFbankBins);
         this->_m_filterBankFilterLast =
-                std::vector<int32_t>(this->_m_params.m_numFbankBins);
+                std::vector<uint32_t>(this->_m_params.m_numFbankBins);
 
         for (size_t bin = 0; bin < this->_m_params.m_numFbankBins; bin++) {
             float leftMel = melLowFreq + bin * melFreqDelta;
             float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
             float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
 
-            int32_t firstIndex = -1;
-            int32_t lastIndex = -1;
+            uint32_t firstIndex = 0;
+            uint32_t lastIndex = 0;
+            bool firstIndexFound = false;
             const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->_m_params.m_useHtkMethod);
 
             for (size_t i = 0; i < numFftBins; ++i) {
@@ -287,8 +289,9 @@
                     }
 
                     thisBin[i] = weight * normaliser;
-                    if (firstIndex == -1) {
+                    if (!firstIndexFound) {
                         firstIndex = i;
+                        firstIndexFound = true;
                     }
                     lastIndex = i;
                 }
@@ -298,7 +301,7 @@
             this->_m_filterBankFilterLast[bin] = lastIndex;
 
             /* Copy the part we care about. */
-            for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex; ++i) {
                 melFilterBank[bin].push_back(thisBin[i]);
             }
         }
diff --git a/source/use_case/ad/src/UseCaseHandler.cc b/source/use_case/ad/src/UseCaseHandler.cc
index 1c15595..8f86966 100644
--- a/source/use_case/ad/src/UseCaseHandler.cc
+++ b/source/use_case/ad/src/UseCaseHandler.cc
@@ -32,7 +32,7 @@
     * @brief           Helper function to increment current audio clip index
     * @param[in/out]   ctx     pointer to the application context object
     **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the audio clip index
@@ -40,7 +40,7 @@
      * @param[in]       idx     value to be set
      * @return          true if index is set, false otherwise
      **/
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents inference results using the data presentation
@@ -50,7 +50,7 @@
      * @param[in]       threhsold   if larger than this value we have an anomaly
      * @return          true if successful, false otherwise
      **/
-    static bool _PresentInferenceResult(hal_platform& platform, float result, float threshold);
+    static bool PresentInferenceResult(hal_platform& platform, float result, float threshold);
 
     /**
      * @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -87,7 +87,7 @@
 
         /* If the request has a valid size, set the audio index */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+            if (!SetAppCtxClipIdx(ctx, clipIndex)) {
                 return false;
             }
         }
@@ -216,20 +216,20 @@
                     dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
 
             ctx.Set<float>("result", result);
-            if (!_PresentInferenceResult(platform, result, scoreThreshold)) {
+            if (!PresentInferenceResult(platform, result, scoreThreshold)) {
                 return false;
             }
 
             profiler.PrintProfilingResult();
 
-            _IncrementAppCtxClipIdx(ctx);
+            IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
         return true;
     }
 
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
     {
         auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
 
@@ -241,7 +241,7 @@
         ctx.Set<uint32_t>("clipIndex", curAudioIdx);
     }
 
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -252,7 +252,7 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform, float result, float threshold)
+    static bool PresentInferenceResult(hal_platform& platform, float result, float threshold)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -275,7 +275,7 @@
 
         platform.data_psn->present_data_text(
                 resultStr.c_str(), resultStr.size(),
-                dataPsnTxtStartX1, rowIdx1, 0);
+                dataPsnTxtStartX1, rowIdx1, false);
 
         info("%s\n", resultStr.c_str());
 
@@ -297,8 +297,8 @@
      */
     template<class T>
     std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)>
-    _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                 std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+    FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+                std::function<std::vector<T> (std::vector<int16_t>& )> compute)
     {
         /* Feature cache to be captured by lambda function*/
         static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -335,24 +335,24 @@
     }
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
-    _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+    FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+                        size_t cacheSize,
+                        std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+
+    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
+    FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
                          size_t cacheSize,
-                         std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+                         std::function<std::vector<uint8_t> (std::vector<int16_t>&)> compute);
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
-    _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<uint8_t> (std::vector<int16_t>&)> compute);
-
-    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
-    _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<int16_t> (std::vector<int16_t>&)> compute);
+    FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+                         size_t cacheSize,
+                         std::function<std::vector<int16_t> (std::vector<int16_t>&)> compute);
 
     template std::function<void(std::vector<int16_t>&, size_t, bool, size_t, size_t)>
-    _FeatureCalc<float>(TfLiteTensor *inputTensor,
-                        size_t cacheSize,
-                        std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+    FeatureCalc<float>(TfLiteTensor *inputTensor,
+                       size_t cacheSize,
+                       std::function<std::vector<float>(std::vector<int16_t>&)> compute);
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)>
@@ -370,38 +370,41 @@
 
             switch (inputTensor->type) {
                 case kTfLiteInt8: {
-                    melSpecFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
-                                                              cacheSize,
-                                                           [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
-                                                               return melSpec.MelSpecComputeQuant<int8_t>(audioDataWindow,
-                                                                       quantScale,
-                                                                       quantOffset,
-                                                                       trainingMean);
-                                                           }
+                    melSpecFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+                                                             cacheSize,
+                                                             [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+                                                                 return melSpec.MelSpecComputeQuant<int8_t>(
+                                                                         audioDataWindow,
+                                                                         quantScale,
+                                                                         quantOffset,
+                                                                         trainingMean);
+                                                             }
                     );
                     break;
                 }
                 case kTfLiteUInt8: {
-                    melSpecFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
-                                                               cacheSize,
-                                                            [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
-                                                                return melSpec.MelSpecComputeQuant<uint8_t>(audioDataWindow,
-                                                                        quantScale,
-                                                                        quantOffset,
-                                                                        trainingMean);
-                                                            }
+                    melSpecFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+                                                              cacheSize,
+                                                              [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+                                                                  return melSpec.MelSpecComputeQuant<uint8_t>(
+                                                                          audioDataWindow,
+                                                                          quantScale,
+                                                                          quantOffset,
+                                                                          trainingMean);
+                                                              }
                     );
                     break;
                 }
                 case kTfLiteInt16: {
-                    melSpecFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
-                                                               cacheSize,
-                                                            [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
-                                                                return melSpec.MelSpecComputeQuant<int16_t>(audioDataWindow,
-                                                                        quantScale,
-                                                                        quantOffset,
-                                                                        trainingMean);
-                                                            }
+                    melSpecFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+                                                              cacheSize,
+                                                              [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+                                                                  return melSpec.MelSpecComputeQuant<int16_t>(
+                                                                          audioDataWindow,
+                                                                          quantScale,
+                                                                          quantOffset,
+                                                                          trainingMean);
+                                                              }
                     );
                     break;
                 }
@@ -411,12 +414,14 @@
 
 
         } else {
-            melSpecFeatureCalc = melSpecFeatureCalc = _FeatureCalc<float>(inputTensor,
-                                                                    cacheSize,
-                                                                    [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
-                                                                        return melSpec.ComputeMelSpec(audioDataWindow,
-                                                                                                      trainingMean);
-                                                                    });
+            melSpecFeatureCalc = melSpecFeatureCalc = FeatureCalc<float>(inputTensor,
+                                                                         cacheSize,
+                                                                         [=, &melSpec](
+                                                                                 std::vector<int16_t>& audioDataWindow) {
+                                                                             return melSpec.ComputeMelSpec(
+                                                                                     audioDataWindow,
+                                                                                     trainingMean);
+                                                                         });
         }
         return melSpecFeatureCalc;
     }
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp
index 1a63814..2c97a39 100644
--- a/source/use_case/asr/include/AsrClassifier.hpp
+++ b/source/use_case/asr/include/AsrClassifier.hpp
@@ -51,9 +51,9 @@
          * @return      true if successful, false otherwise.
          **/
         template<typename T>
-        bool _GetTopResults(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
+        bool GetTopResults(TfLiteTensor* tensor,
+                           std::vector<ClassificationResult>& vecResults,
+                           const std::vector <std::string>& labels, double scale, double zeroPoint);
     };
 
 } /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterMfcc.hpp b/source/use_case/asr/include/Wav2LetterMfcc.hpp
index 3cb43b9..b5a21d3 100644
--- a/source/use_case/asr/include/Wav2LetterMfcc.hpp
+++ b/source/use_case/asr/include/Wav2LetterMfcc.hpp
@@ -60,8 +60,8 @@
         bool ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&              filterBankFilterFirst,
+            std::vector<uint32_t>&              filterBankFilterLast,
             std::vector<float>&                 melEnergies) override;
 
         /**
diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp
index b801e10..4c62578 100644
--- a/source/use_case/asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/asr/include/Wav2LetterModel.hpp
@@ -49,10 +49,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int _ms_maxOpCnt = 5;
+        static constexpr int ms_maxOpCnt = 5;
 
         /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
index 69567a3..e16d35b 100644
--- a/source/use_case/asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
@@ -72,33 +72,33 @@
          *              initialised.
          * @return      true if valid, false otherwise.
          */
-        bool _IsInputValid(TfLiteTensor*  tensor,
-                           uint32_t axisIdx) const;
+        bool IsInputValid(TfLiteTensor*  tensor,
+                          const uint32_t axisIdx) const;
 
         /**
          * @brief       Gets the tensor data element size in bytes based
          *              on the tensor type.
          * @return      Size in bytes, 0 if not supported.
          */
-        uint32_t _GetTensorElementSize(TfLiteTensor* tensor);
+        static uint32_t GetTensorElementSize(TfLiteTensor* tensor);
 
         /**
          * @brief       Erases sections from the data assuming row-wise
          *              arrangement along the context axis.
          * @return      true if successful, false otherwise.
          */
-        bool _EraseSectionsRowWise(uint8_t* ptrData,
-                                   uint32_t strideSzBytes,
-                                   bool lastIteration);
+        bool EraseSectionsRowWise(uint8_t* ptrData,
+                                  const uint32_t strideSzBytes,
+                                  const bool lastIteration);
 
         /**
          * @brief       Erases sections from the data assuming col-wise
          *              arrangement along the context axis.
          * @return      true if successful, false otherwise.
          */
-        bool _EraseSectionsColWise(uint8_t* ptrData,
-                                   uint32_t strideSzBytes,
-                                   bool lastIteration);
+        static bool EraseSectionsColWise(const uint8_t* ptrData,
+                                  const uint32_t strideSzBytes,
+                                  const bool lastIteration);
     };
 
 } /* namespace asr */
diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
index 8a4e0b7..10512b9 100644
--- a/source/use_case/asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
@@ -74,16 +74,16 @@
           * @param[out] delta2   Result of the second diff computation.
           * @return     true if successful, false otherwise.
           */
-         static bool _ComputeDeltas(Array2d<float>& mfcc,
-                                    Array2d<float>& delta1,
-                                    Array2d<float>& delta2);
+         static bool ComputeDeltas(Array2d<float>& mfcc,
+                                   Array2d<float>& delta1,
+                                   Array2d<float>& delta2);
 
         /**
          * @brief       Given a 2D vector of floats, computes the mean.
          * @param[in]   vec   Vctor of vector of floats.
          * @return      Mean value.
          */
-        static float _GetMean(Array2d<float>& vec);
+        static float GetMean(Array2d<float>& vec);
 
         /**
          * @brief       Given a 2D vector of floats, computes the stddev.
@@ -91,20 +91,20 @@
          * @param[in]   mean   Mean value of the vector passed in.
          * @return      stddev value.
          */
-        static float _GetStdDev(Array2d<float>& vec,
-                                float mean);
+        static float GetStdDev(Array2d<float>& vec,
+                               const float mean);
 
         /**
          * @brief           Given a 2D vector of floats, normalises it using
          *                  the mean and the stddev.
          * @param[in,out]   vec   Vector of vector of floats.
          */
-        static void _NormaliseVec(Array2d<float>& vec);
+        static void NormaliseVec(Array2d<float>& vec);
 
         /**
          * @brief   Normalises the MFCC and delta buffers.
          */
-        void _Normalise();
+        void Normalise();
 
         /**
          * @brief       Given the quantisation and data type limits, computes
@@ -116,7 +116,7 @@
          * @param[in]   maxVal        Numerical limit - maximum.
          * @return      Floating point quantised value.
          */
-        static float _GetQuantElem(
+        static float GetQuantElem(
                 float     elem,
                 float     quantScale,
                 int       quantOffset,
@@ -137,7 +137,7 @@
          * @param[in]   quantOffset   Quantisation offset.
          */
         template <typename T>
-        bool _Quantise(
+        bool Quantise(
                 T *             outputBuf,
                 const uint32_t  outputBufSz,
                 const float     quantScale,
@@ -161,15 +161,15 @@
             /* Need to transpose while copying and concatenating the tensor. */
             for (uint32_t j = 0; j < this->_m_numFeatVectors; ++j) {
                 for (uint32_t i = 0; i < this->_m_numMfccFeats; ++i) {
-                    *outputBufMfcc++ = static_cast<T>(Preprocess::_GetQuantElem(
-                                        this->_m_mfccBuf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
-                    *outputBufD1++ = static_cast<T>(Preprocess::_GetQuantElem(
-                                        this->_m_delta1Buf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
-                    *outputBufD2++ = static_cast<T>(Preprocess::_GetQuantElem(
-                                        this->_m_delta2Buf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
+                    *outputBufMfcc++ = static_cast<T>(Preprocess::GetQuantElem(
+                            this->_m_mfccBuf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
+                    *outputBufD1++ = static_cast<T>(Preprocess::GetQuantElem(
+                            this->_m_delta1Buf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
+                    *outputBufD2++ = static_cast<T>(Preprocess::GetQuantElem(
+                            this->_m_delta2Buf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
                 }
                 outputBufMfcc += ptrIncr;
                 outputBufD1 += ptrIncr;
diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc
index 7377d30..df26a7f 100644
--- a/source/use_case/asr/src/AsrClassifier.cc
+++ b/source/use_case/asr/src/AsrClassifier.cc
@@ -21,13 +21,18 @@
 #include "Wav2LetterModel.hpp"
 
 template<typename T>
-bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint)
+bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor,
+                                            std::vector<ClassificationResult>& vecResults,
+                                            const std::vector <std::string>& labels, double scale, double zeroPoint)
 {
     const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx];
     const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx];
 
+    if (nLetters != labels.size()) {
+        printf("Output size doesn't match the labels' size\n");
+        return false;
+    }
+
     /* NOTE: tensor's size verification against labels should be
      *       checked by the calling/public function. */
     if (nLetters < 1) {
@@ -58,12 +63,12 @@
 
     return true;
 }
-template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
-template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+                                                              std::vector<ClassificationResult>& vecResults,
+                                                              const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+                                                             std::vector<ClassificationResult>& vecResults,
+                                                             const std::vector <std::string>& labels, double scale, double zeroPoint);
 
 bool arm::app::AsrClassifier::GetClassificationResults(
             TfLiteTensor* outputTensor,
@@ -104,16 +109,16 @@
 
         switch (outputTensor->type) {
             case kTfLiteUInt8:
-                resultState = this->_GetTopResults<uint8_t>(
-                                        outputTensor, vecResults,
-                                        labels, quantParams.scale,
-                                        quantParams.offset);
+                resultState = this->GetTopResults<uint8_t>(
+                        outputTensor, vecResults,
+                        labels, quantParams.scale,
+                        quantParams.offset);
                 break;
             case kTfLiteInt8:
-                resultState = this->_GetTopResults<int8_t>(
-                                        outputTensor, vecResults,
-                                        labels, quantParams.scale,
-                                        quantParams.offset);
+                resultState = this->GetTopResults<int8_t>(
+                        outputTensor, vecResults,
+                        labels, quantParams.scale,
+                        quantParams.offset);
                 break;
             default:
                 printf_err("Tensor type %s not supported by classifier\n",
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 5d3157a..efaefc2 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -35,7 +35,7 @@
     * @brief           Helper function to increment current audio clip index.
     * @param[in,out]   ctx   Pointer to the application context object.
     **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the audio clip index.
@@ -43,7 +43,7 @@
      * @param[in]       idx   Value to be set.
      * @return          true if index is set, false otherwise.
      **/
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents inference results using the data presentation
@@ -54,7 +54,7 @@
      *                              otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise.
      **/
-    static bool _PresentInferenceResult(
+    static bool PresentInferenceResult(
                     hal_platform& platform,
                     const std::vector<arm::app::asr::AsrResult>& results);
 
@@ -71,7 +71,7 @@
 
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+            if (!SetAppCtxClipIdx(ctx, clipIndex)) {
                 return false;
             }
         }
@@ -207,20 +207,20 @@
 
             ctx.Set<std::vector<arm::app::asr::AsrResult>>("results", results);
 
-            if (!_PresentInferenceResult(platform, results)) {
+            if (!PresentInferenceResult(platform, results)) {
                 return false;
             }
 
             profiler.PrintProfilingResult();
 
-            _IncrementAppCtxClipIdx(ctx);
+            IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
         return true;
     }
 
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
     {
         auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
 
@@ -232,7 +232,7 @@
         ctx.Set<uint32_t>("clipIndex", curAudioIdx);
     }
 
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -244,8 +244,8 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform,
-                                        const std::vector<arm::app::asr::AsrResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       const std::vector<arm::app::asr::AsrResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 60;
diff --git a/source/use_case/asr/src/Wav2LetterMfcc.cc b/source/use_case/asr/src/Wav2LetterMfcc.cc
index 92c91bc..0eb152a 100644
--- a/source/use_case/asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/asr/src/Wav2LetterMfcc.cc
@@ -27,8 +27,8 @@
     bool Wav2LetterMFCC::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&               filterBankFilterFirst,
+            std::vector<uint32_t>&               filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -41,11 +41,14 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
-            float melEnergy = 1e-10;  /* Avoid log of zero at later stages, same value used in librosa. */
-            const int32_t firstIndex = filterBankFilterFirst[bin];
-            const int32_t lastIndex = filterBankFilterLast[bin];
+            auto end = melFilterBank[bin].end();
+            /* Avoid log of zero at later stages, same value used in librosa.
+             * The number was used during our default wav2letter model training. */
+            float melEnergy = 1e-10;
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
                 melEnergy += (*filterBankIter++ * fftVec[i]);
             }
 
@@ -73,7 +76,7 @@
 
         /* Scale the log values and get the max. */
         for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
-                  iterM != melEnergies.end(); ++iterM, ++iterL) {
+                  iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
 
             *iterM = *iterL * multiplier;
 
@@ -86,8 +89,8 @@
         /* Clamp the mel energies. */
         constexpr float maxDb = 80.0;
         const float clampLevelLowdB = maxMelEnergy - maxDb;
-        for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) {
-            *iter = std::max(*iter, clampLevelLowdB);
+        for (float& melEnergy : melEnergies) {
+            melEnergy = std::max(melEnergy, clampLevelLowdB);
         }
     }
 
diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc
index 60ee51e..9157a6f 100644
--- a/source/use_case/asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc
@@ -39,13 +39,13 @@
                             const bool      lastIteration)
     {
         /* Basic checks. */
-        if (!this->_IsInputValid(tensor, axisIdx)) {
+        if (!this->IsInputValid(tensor, axisIdx)) {
             return false;
         }
 
         /* Irrespective of tensor type, we use unsigned "byte" */
         uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor);
-        const uint32_t elemSz = this->_GetTensorElementSize(tensor);
+        const uint32_t elemSz = this->GetTensorElementSize(tensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
@@ -59,13 +59,15 @@
         /* Which axis do we need to process? */
         switch (axisIdx) {
             case arm::app::Wav2LetterModel::ms_outputRowsIdx:
-                return this->_EraseSectionsRowWise(ptrData,
-                        elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
-                        lastIteration);
+                return this->EraseSectionsRowWise(ptrData,
+                                                  elemSz *
+                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
+                                                  lastIteration);
             case arm::app::Wav2LetterModel::ms_outputColsIdx:
-                return this->_EraseSectionsColWise(ptrData,
-                        elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx],
-                        lastIteration);
+                return this->EraseSectionsColWise(ptrData,
+                                                  elemSz *
+                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx],
+                                                  lastIteration);
             default:
                 printf_err("Unsupported axis index: %u\n", axisIdx);
         }
@@ -73,8 +75,8 @@
         return false;
     }
 
-    bool Postprocess::_IsInputValid(TfLiteTensor*  tensor,
-                                    const uint32_t axisIdx) const
+    bool Postprocess::IsInputValid(TfLiteTensor*  tensor,
+                                   const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -96,17 +98,15 @@
         return true;
     }
 
-    uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor*  tensor)
+    uint32_t Postprocess::GetTensorElementSize(TfLiteTensor*  tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
-                return 1;
             case kTfLiteInt8:
                 return 1;
             case kTfLiteInt16:
                 return 2;
             case kTfLiteInt32:
-                return 4;
             case kTfLiteFloat32:
                 return 4;
             default:
@@ -117,7 +117,7 @@
         return 0;
     }
 
-    bool Postprocess::_EraseSectionsRowWise(
+    bool Postprocess::EraseSectionsRowWise(
                         uint8_t*         ptrData,
                         const uint32_t   strideSzBytes,
                         const bool       lastIteration)
@@ -154,8 +154,8 @@
         return true;
     }
 
-    bool Postprocess::_EraseSectionsColWise(
-                        uint8_t*         ptrData,
+    bool Postprocess::EraseSectionsColWise(
+                        const uint8_t*         ptrData,
                         const uint32_t   strideSzBytes,
                         const bool       lastIteration)
     {
diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc
index e46cca3..d65ea75 100644
--- a/source/use_case/asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc
@@ -88,12 +88,12 @@
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        this->_ComputeDeltas(this->_m_mfccBuf,
-                             this->_m_delta1Buf,
-                             this->_m_delta2Buf);
+        Preprocess::ComputeDeltas(this->_m_mfccBuf,
+                            this->_m_delta1Buf,
+                            this->_m_delta2Buf);
 
         /* Normalise. */
-        this->_Normalise();
+        this->Normalise();
 
         /* Quantise. */
         QuantParams quantParams = GetTensorQuantParams(tensor);
@@ -105,11 +105,11 @@
 
         switch(tensor->type) {
             case kTfLiteUInt8:
-                return this->_Quantise<uint8_t>(
+                return this->Quantise<uint8_t>(
                         tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             case kTfLiteInt8:
-                return this->_Quantise<int8_t>(
+                return this->Quantise<int8_t>(
                         tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             default:
@@ -120,9 +120,9 @@
         return false;
     }
 
-    bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
-                                    Array2d<float>& delta1,
-                                    Array2d<float>& delta2)
+    bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+                                   Array2d<float>& delta1,
+                                   Array2d<float>& delta2)
     {
         const std::vector <float> delta1Coeffs =
             {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
@@ -175,20 +175,20 @@
         return true;
     }
 
-    float Preprocess::_GetMean(Array2d<float>& vec)
+    float Preprocess::GetMean(Array2d<float>& vec)
     {
         return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
     }
 
-    float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+    float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
     {
         return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
     }
 
-    void Preprocess::_NormaliseVec(Array2d<float>& vec)
+    void Preprocess::NormaliseVec(Array2d<float>& vec)
     {
-        auto mean = Preprocess::_GetMean(vec);
-        auto stddev = Preprocess::_GetStdDev(vec, mean);
+        auto mean = Preprocess::GetMean(vec);
+        auto stddev = Preprocess::GetStdDev(vec, mean);
 
         debug("Mean: %f, Stddev: %f\n", mean, stddev);
         if (stddev == 0) {
@@ -204,14 +204,14 @@
         }
     }
 
-    void Preprocess::_Normalise()
+    void Preprocess::Normalise()
     {
-        Preprocess::_NormaliseVec(this->_m_mfccBuf);
-        Preprocess::_NormaliseVec(this->_m_delta1Buf);
-        Preprocess::_NormaliseVec(this->_m_delta2Buf);
+        Preprocess::NormaliseVec(this->_m_mfccBuf);
+        Preprocess::NormaliseVec(this->_m_delta1Buf);
+        Preprocess::NormaliseVec(this->_m_delta2Buf);
     }
 
-    float Preprocess::_GetQuantElem(
+    float Preprocess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,
diff --git a/source/use_case/img_class/include/MobileNetModel.hpp b/source/use_case/img_class/include/MobileNetModel.hpp
index f0521ce..2540564 100644
--- a/source/use_case/img_class/include/MobileNetModel.hpp
+++ b/source/use_case/img_class/include/MobileNetModel.hpp
@@ -43,10 +43,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int _ms_maxOpCnt = 7;
+        static constexpr int ms_maxOpCnt = 7;
 
         /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index f7e83f5..ffeb860 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -35,13 +35,13 @@
     * @param[out]      inputTensor   Pointer to the input tensor to be populated.
     * @return          true if tensor is loaded, false otherwise.
     **/
-    static bool _LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor);
+    static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor);
 
     /**
      * @brief           Helper function to increment current image index.
      * @param[in,out]   ctx   Pointer to the application context object.
      **/
-    static void _IncrementAppCtxImageIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxImageIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the image index.
@@ -49,7 +49,7 @@
      * @param[in]       idx   Value to be set.
      * @return          true if index is set, false otherwise.
      **/
-    static bool _SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents inference results using the data presentation
@@ -60,8 +60,8 @@
      *                              otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise.
      **/
-    static bool _PresentInferenceResult(hal_platform& platform,
-                                        const std::vector<ClassificationResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       const std::vector<ClassificationResult>& results);
 
     /**
      * @brief           Helper function to convert a UINT8 image to INT8 format.
@@ -89,7 +89,7 @@
 
         /* If the request has a valid size, set the image index. */
         if (imgIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxImageIdx(ctx, imgIndex)) {
+            if (!SetAppCtxImageIdx(ctx, imgIndex)) {
                 return false;
             }
         }
@@ -124,7 +124,7 @@
             std::string str_inf{"Running inference... "};
 
             /* Copy over the data. */
-            _LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
+            LoadImageIntoTensor(ctx.Get<uint32_t>("imgIndex"), inputTensor);
 
             /* Display this image on the LCD. */
             platform.data_psn->present_data_image(
@@ -164,20 +164,20 @@
             arm::app::DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
 
-            if (!_PresentInferenceResult(platform, results)) {
+            if (!PresentInferenceResult(platform, results)) {
                 return false;
             }
 
             profiler.PrintProfilingResult();
 
-            _IncrementAppCtxImageIdx(ctx);
+            IncrementAppCtxImageIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("imgIndex") != curImIdx);
 
         return true;
     }
 
-    static bool _LoadImageIntoTensor(const uint32_t imIdx, TfLiteTensor* inputTensor)
+    static bool LoadImageIntoTensor(uint32_t imIdx, TfLiteTensor* inputTensor)
     {
         const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
                               inputTensor->bytes : IMAGE_DATA_SIZE;
@@ -193,7 +193,7 @@
         return true;
     }
 
-    static void _IncrementAppCtxImageIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxImageIdx(ApplicationContext& ctx)
     {
         auto curImIdx = ctx.Get<uint32_t>("imgIndex");
 
@@ -205,7 +205,7 @@
         ctx.Set<uint32_t>("imgIndex", curImIdx);
     }
 
-    static bool _SetAppCtxImageIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxImageIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -216,8 +216,8 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform,
-                                        const std::vector<ClassificationResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       const std::vector<ClassificationResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 150;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
diff --git a/source/use_case/kws/include/DsCnnModel.hpp b/source/use_case/kws/include/DsCnnModel.hpp
index a4e7110..e9ac18c 100644
--- a/source/use_case/kws/include/DsCnnModel.hpp
+++ b/source/use_case/kws/include/DsCnnModel.hpp
@@ -47,10 +47,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int _ms_maxOpCnt = 8;
+        static constexpr int ms_maxOpCnt = 8;
 
         /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index d2cba55..4011df6 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -37,7 +37,7 @@
     * @brief            Helper function to increment current audio clip index.
     * @param[in,out]    ctx   Pointer to the application context object.
     **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the audio clip index.
@@ -45,7 +45,7 @@
      * @param[in]       idx   Value to be set.
      * @return          true if index is set, false otherwise.
      **/
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents inference results using the data presentation
@@ -56,8 +56,8 @@
      *                              otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise.
      **/
-    static bool _PresentInferenceResult(hal_platform& platform,
-                                        const std::vector<arm::app::kws::KwsResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       const std::vector<arm::app::kws::KwsResult>& results);
 
     /**
      * @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -96,7 +96,7 @@
 
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+            if (!SetAppCtxClipIdx(ctx, clipIndex)) {
                 return false;
             }
         }
@@ -240,20 +240,20 @@
 
             ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
 
-            if (!_PresentInferenceResult(platform, results)) {
+            if (!PresentInferenceResult(platform, results)) {
                 return false;
             }
 
             profiler.PrintProfilingResult();
 
-            _IncrementAppCtxClipIdx(ctx);
+            IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
         return true;
     }
 
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
     {
         auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
 
@@ -265,7 +265,7 @@
         ctx.Set<uint32_t>("clipIndex", curAudioIdx);
     }
 
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -276,8 +276,8 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform,
-                                        const std::vector<arm::app::kws::KwsResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       const std::vector<arm::app::kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -345,8 +345,8 @@
      */
     template<class T>
     std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
-    _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                 std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+    FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+                std::function<std::vector<T> (std::vector<int16_t>& )> compute)
     {
         /* Feature cache to be captured by lambda function. */
         static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -378,24 +378,24 @@
     }
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-        _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+        FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
                             size_t cacheSize,
                             std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-        _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
-                              size_t cacheSize,
-                              std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
+        FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
+                             size_t cacheSize,
+                             std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-        _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
-                              size_t cacheSize,
-                              std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+        FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+                             size_t cacheSize,
+                             std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
-        _FeatureCalc<float>(TfLiteTensor *inputTensor,
-                            size_t cacheSize,
-                            std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+        FeatureCalc<float>(TfLiteTensor* inputTensor,
+                           size_t cacheSize,
+                           std::function<std::vector<float>(std::vector<int16_t>&)> compute);
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
@@ -413,19 +413,19 @@
 
             switch (inputTensor->type) {
                 case kTfLiteInt8: {
-                    mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
-                                                           cacheSize,
-                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                               return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
-                                                                                                    quantScale,
-                                                                                                    quantOffset);
-                                                           }
+                    mfccFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+                                                          cacheSize,
+                                                          [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                              return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+                                                                                                   quantScale,
+                                                                                                   quantOffset);
+                                                          }
                     );
                     break;
                 }
                 case kTfLiteUInt8: {
-                    mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
-                                                            cacheSize,
+                    mfccFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+                                                           cacheSize,
                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
                                                                return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
                                                                                                      quantScale,
@@ -435,13 +435,13 @@
                     break;
                 }
                 case kTfLiteInt16: {
-                    mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
-                                                            cacheSize,
-                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
-                                                                                                      quantScale,
-                                                                                                      quantOffset);
-                                                            }
+                    mfccFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+                                                           cacheSize,
+                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                               return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+                                                                                                     quantScale,
+                                                                                                     quantOffset);
+                                                           }
                     );
                     break;
                 }
@@ -451,11 +451,11 @@
 
 
         } else {
-            mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
-                                                                    cacheSize,
-                                                                    [&mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                        return mfcc.MfccCompute(audioDataWindow);
-                                                                    });
+            mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
+                                                                   cacheSize,
+                                                                   [&mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                                       return mfcc.MfccCompute(audioDataWindow);
+                                                                   });
         }
         return mfccFeatureCalc;
     }
diff --git a/source/use_case/kws_asr/include/AsrClassifier.hpp b/source/use_case/kws_asr/include/AsrClassifier.hpp
index de18aa8..7dbb6e9 100644
--- a/source/use_case/kws_asr/include/AsrClassifier.hpp
+++ b/source/use_case/kws_asr/include/AsrClassifier.hpp
@@ -53,9 +53,9 @@
          * @return      true if successful, false otherwise.
          **/
         template<typename T>
-        bool _GetTopResults(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
+        bool GetTopResults(TfLiteTensor* tensor,
+                           std::vector<ClassificationResult>& vecResults,
+                           const std::vector <std::string>& labels, double scale, double zeroPoint);
     };
 
 } /* namespace app */
diff --git a/source/use_case/kws_asr/include/DsCnnModel.hpp b/source/use_case/kws_asr/include/DsCnnModel.hpp
index 150a48c..f9d4357 100644
--- a/source/use_case/kws_asr/include/DsCnnModel.hpp
+++ b/source/use_case/kws_asr/include/DsCnnModel.hpp
@@ -55,10 +55,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int _ms_maxOpCnt = 10;
+        static constexpr int ms_maxOpCnt = 10;
 
         /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
index 0852cbf..75d75da 100644
--- a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
@@ -60,8 +60,8 @@
         bool ApplyMelFilterBank(
                 std::vector<float>&                 fftVec,
                 std::vector<std::vector<float>>&    melFilterBank,
-                std::vector<int32_t>&               filterBankFilterFirst,
-                std::vector<int32_t>&               filterBankFilterLast,
+                std::vector<uint32_t>&              filterBankFilterFirst,
+                std::vector<uint32_t>&              filterBankFilterLast,
                 std::vector<float>&                 melEnergies) override;
 
         /**
@@ -103,6 +103,7 @@
                 const float&   leftMel,
                 const float&   rightMel,
                 bool     useHTKMethod) override;
+
     };
 
 } /* namespace audio */
diff --git a/source/use_case/kws_asr/include/Wav2LetterModel.hpp b/source/use_case/kws_asr/include/Wav2LetterModel.hpp
index fb701ea..9a86bd9 100644
--- a/source/use_case/kws_asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterModel.hpp
@@ -55,10 +55,10 @@
 
     private:
         /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int _ms_maxOpCnt = 5;
+        static constexpr int ms_maxOpCnt = 5;
 
         /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<_ms_maxOpCnt> _m_opResolver;
+        tflite::MicroMutableOpResolver<ms_maxOpCnt> _m_opResolver;
     };
 
 } /* namespace app */
diff --git a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
index 3a9d401..fe60923 100644
--- a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
@@ -72,24 +72,24 @@
          *              initialised.
          * @return      true if valid, false otherwise.
          */
-        bool _IsInputValid(TfLiteTensor*  tensor,
-                           uint32_t axisIdx) const;
+        bool IsInputValid(TfLiteTensor*  tensor,
+                          const uint32_t axisIdx) const;
 
         /**
          * @brief       Gets the tensor data element size in bytes based
          *              on the tensor type.
          * @return      Size in bytes, 0 if not supported.
          */
-        uint32_t _GetTensorElementSize(TfLiteTensor* tensor);
+        uint32_t GetTensorElementSize(TfLiteTensor* tensor);
 
         /**
          * @brief       Erases sections from the data assuming row-wise
          *              arrangement along the context axis.
          * @return      true if successful, false otherwise.
          */
-        bool _EraseSectionsRowWise(uint8_t* ptrData,
-                                   uint32_t strideSzBytes,
-                                   bool lastIteration);
+        bool EraseSectionsRowWise(uint8_t* ptrData,
+                                  const uint32_t strideSzBytes,
+                                  const bool lastIteration);
 
     };
 
diff --git a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
index 3ffabb4..cf40fa8 100644
--- a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
@@ -75,16 +75,16 @@
           *
           * @return true if successful, false otherwise.
           */
-         static bool _ComputeDeltas(Array2d<float>& mfcc,
-                                    Array2d<float>& delta1,
-                                    Array2d<float>& delta2);
+         static bool ComputeDeltas(Array2d<float>& mfcc,
+                                   Array2d<float>& delta1,
+                                   Array2d<float>& delta2);
 
         /**
          * @brief       Given a 2D vector of floats, computes the mean.
          * @param[in]   vec   Vector of vector of floats.
          * @return      Mean value.
          */
-        static float _GetMean(Array2d<float>& vec);
+        static float GetMean(Array2d<float>& vec);
 
         /**
          * @brief       Given a 2D vector of floats, computes the stddev.
@@ -92,20 +92,20 @@
          * @param[in]   mean   Mean value of the vector passed in.
          * @return      stddev value.
          */
-        static float _GetStdDev(Array2d<float>& vec,
-                                float mean);
+        static float GetStdDev(Array2d<float>& vec,
+                               const float mean);
 
         /**
          * @brief           Given a 2D vector of floats, normalises it using
          *                  the mean and the stddev
          * @param[in,out]   vec   Vector of vector of floats.
          */
-        static void _NormaliseVec(Array2d<float>& vec);
+        static void NormaliseVec(Array2d<float>& vec);
 
         /**
          * @brief       Normalises the MFCC and delta buffers.
          */
-        void _Normalise();
+        void Normalise();
 
         /**
          * @brief       Given the quantisation and data type limits, computes
@@ -117,7 +117,7 @@
          * @param[in]   maxVal          Numerical limit - maximum.
          * @return      Floating point quantised value.
          */
-        static float _GetQuantElem(
+        static float GetQuantElem(
                 float     elem,
                 float     quantScale,
                 int       quantOffset,
@@ -138,7 +138,7 @@
          * @param[in]   quantOffset     Quantisation offset.
          */
         template <typename T>
-        bool _Quantise(
+        bool Quantise(
                 T *             outputBuf,
                 const uint32_t  outputBufSz,
                 const float     quantScale,
@@ -163,15 +163,15 @@
              * the tensor. */
             for (uint32_t j = 0; j < this->_m_numFeatVectors; ++j) {
                 for (uint32_t i = 0; i < this->_m_numMfccFeats; ++i) {
-                    *outputBufMfcc++ = static_cast<T>(this->_GetQuantElem(
-                                        this->_m_mfccBuf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
-                    *outputBufD1++ = static_cast<T>(this->_GetQuantElem(
-                                        this->_m_delta1Buf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
-                    *outputBufD2++ = static_cast<T>(this->_GetQuantElem(
-                                        this->_m_delta2Buf(i, j), quantScale,
-                                        quantOffset, minVal, maxVal));
+                    *outputBufMfcc++ = static_cast<T>(this->GetQuantElem(
+                            this->_m_mfccBuf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
+                    *outputBufD1++ = static_cast<T>(this->GetQuantElem(
+                            this->_m_delta1Buf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
+                    *outputBufD2++ = static_cast<T>(this->GetQuantElem(
+                            this->_m_delta2Buf(i, j), quantScale,
+                            quantOffset, minVal, maxVal));
                 }
                 outputBufMfcc += ptrIncr;
                 outputBufD1 += ptrIncr;
diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc
index bc86e09..f1fa6f1 100644
--- a/source/use_case/kws_asr/src/AsrClassifier.cc
+++ b/source/use_case/kws_asr/src/AsrClassifier.cc
@@ -21,13 +21,17 @@
 #include "Wav2LetterModel.hpp"
 
 template<typename T>
-bool arm::app::AsrClassifier::_GetTopResults(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint)
+bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor,
+                                            std::vector<ClassificationResult>& vecResults,
+                                            const std::vector <std::string>& labels, double scale, double zeroPoint)
 {
     const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx];
     const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx];
 
+    if (nLetters != labels.size()) {
+        printf("Output size doesn't match the labels' size\n");
+        return false;
+    }
 
     /* NOTE: tensor's size verification against labels should be
      *       checked by the calling/public function. */
@@ -42,7 +46,7 @@
 
     /* Get the top 1 results. */
     for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) {
-        std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0);
+        std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row], 0);
 
         for (uint32_t j = 1; j < nLetters; ++j) {
             if (top_1.first < tensorData[row + j]) {
@@ -59,12 +63,12 @@
 
     return true;
 }
-template bool arm::app::AsrClassifier::_GetTopResults<uint8_t>(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
-template bool arm::app::AsrClassifier::_GetTopResults<int8_t>(TfLiteTensor* tensor,
-                            std::vector<ClassificationResult>& vecResults,
-                            const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+                                                              std::vector<ClassificationResult>& vecResults,
+                                                              const std::vector <std::string>& labels, double scale, double zeroPoint);
+template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+                                                             std::vector<ClassificationResult>& vecResults,
+                                                             const std::vector <std::string>& labels, double scale, double zeroPoint);
 
 bool arm::app::AsrClassifier::GetClassificationResults(
             TfLiteTensor* outputTensor,
@@ -105,16 +109,16 @@
 
         switch (outputTensor->type) {
             case kTfLiteUInt8:
-                resultState = this->_GetTopResults<uint8_t>(
-                                        outputTensor, vecResults,
-                                        labels, quantParams.scale,
-                                        quantParams.offset);
+                resultState = this->GetTopResults<uint8_t>(
+                        outputTensor, vecResults,
+                        labels, quantParams.scale,
+                        quantParams.offset);
                 break;
             case kTfLiteInt8:
-                resultState = this->_GetTopResults<int8_t>(
-                                        outputTensor, vecResults,
-                                        labels, quantParams.scale,
-                                        quantParams.offset);
+                resultState = this->GetTopResults<int8_t>(
+                        outputTensor, vecResults,
+                        labels, quantParams.scale,
+                        quantParams.offset);
                 break;
             default:
                 printf_err("Tensor type %s not supported by classifier\n",
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index a428210..1edc7c4 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -52,13 +52,7 @@
     * @brief           Helper function to increment current audio clip index
     * @param[in,out]   ctx     pointer to the application context object
     **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
-
-    /**
-    * @brief           Helper function to increment current audio clip index
-    * @param[in,out]   ctx     pointer to the application context object
-    **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the audio clip index
@@ -66,7 +60,7 @@
      * @param[in]       idx     value to be set
      * @return          true if index is set, false otherwise
      **/
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents kws inference results using the data presentation
@@ -77,7 +71,7 @@
      *                              Otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise
      **/
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
 
     /**
      * @brief           Presents asr inference results using the data presentation
@@ -88,7 +82,7 @@
      *                              Otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise
      **/
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
 
     /**
      * @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -212,7 +206,7 @@
         std::string str_inf{"Running KWS inference... "};
         platform.data_psn->present_data_text(
                             str_inf.c_str(), str_inf.size(),
-                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
         info("Running KWS inference on audio clip %u => %s\n",
              currentIndex, get_filename(currentIndex));
@@ -279,9 +273,9 @@
         str_inf = std::string(str_inf.size(), ' ');
         platform.data_psn->present_data_text(
                             str_inf.c_str(), str_inf.size(),
-                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
-        if (!_PresentInferenceResult(platform, kwsResults)) {
+        if (!PresentInferenceResult(platform, kwsResults)) {
             return output;
         }
 
@@ -375,7 +369,7 @@
         std::string str_inf{"Running ASR inference... "};
         platform.data_psn->present_data_text(
                 str_inf.c_str(), str_inf.size(),
-                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
         size_t asrInferenceWindowLen = asrAudioParamsWinLen;
 
@@ -425,7 +419,7 @@
                         str_inf.c_str(), str_inf.size(),
                         dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
         }
-        if (!_PresentInferenceResult(platform, asrResults)) {
+        if (!PresentInferenceResult(platform, asrResults)) {
             return false;
         }
 
@@ -442,7 +436,7 @@
 
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+            if (!SetAppCtxClipIdx(ctx, clipIndex)) {
                 return false;
             }
         }
@@ -463,14 +457,14 @@
                 }
             }
 
-            _IncrementAppCtxClipIdx(ctx);
+            IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
         return true;
     }
 
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
     {
         auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
 
@@ -482,7 +476,7 @@
         ctx.Set<uint32_t>("clipIndex", curAudioIdx);
     }
 
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -493,8 +487,8 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform,
-                std::vector<arm::app::kws::KwsResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       std::vector<arm::app::kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -510,7 +504,7 @@
             std::string topKeyword{"<none>"};
             float score = 0.f;
 
-            if (results[i].m_resultVec.size()) {
+            if (!results[i].m_resultVec.empty()) {
                 topKeyword = results[i].m_resultVec[0].m_label;
                 score = results[i].m_resultVec[0].m_normalisedVal;
             }
@@ -538,7 +532,7 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 80;
@@ -587,8 +581,8 @@
      **/
     template<class T>
     std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
-    _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                 std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+    FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+                std::function<std::vector<T> (std::vector<int16_t>& )> compute)
     {
         /* Feature cache to be captured by lambda function. */
         static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -621,24 +615,24 @@
     }
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+    FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+                        size_t cacheSize,
+                        std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+
+    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+    FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
                          size_t cacheSize,
-                         std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+                         std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
-
-    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+    FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+                         size_t cacheSize,
+                         std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
-    _FeatureCalc<float>(TfLiteTensor* inputTensor,
-                        size_t cacheSize,
-                        std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+    FeatureCalc<float>(TfLiteTensor* inputTensor,
+                       size_t cacheSize,
+                       std::function<std::vector<float>(std::vector<int16_t>&)> compute);
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
@@ -656,35 +650,35 @@
 
             switch (inputTensor->type) {
                 case kTfLiteInt8: {
-                    mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
-                                                           cacheSize,
-                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                               return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
-                                                                                                    quantScale,
-                                                                                                    quantOffset);
-                                                           }
+                    mfccFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+                                                          cacheSize,
+                                                          [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                              return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+                                                                                                   quantScale,
+                                                                                                   quantOffset);
+                                                          }
                     );
                     break;
                 }
                 case kTfLiteUInt8: {
-                    mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
-                                                            cacheSize,
-                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
-                                                                                                      quantScale,
-                                                                                                      quantOffset);
-                                                            }
+                    mfccFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+                                                           cacheSize,
+                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                               return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
+                                                                                                     quantScale,
+                                                                                                     quantOffset);
+                                                           }
                     );
                     break;
                 }
                 case kTfLiteInt16: {
-                    mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
-                                                            cacheSize,
-                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
-                                                                                                      quantScale,
-                                                                                                      quantOffset);
-                                                            }
+                    mfccFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+                                                           cacheSize,
+                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                               return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+                                                                                                     quantScale,
+                                                                                                     quantOffset);
+                                                           }
                     );
                     break;
                 }
@@ -694,11 +688,11 @@
 
 
         } else {
-            mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
-                                                                    cacheSize,
-                                                                    [&mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                        return mfcc.MfccCompute(audioDataWindow);
-                                                                    });
+            mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
+                                                                   cacheSize,
+                                                                   [&mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                                       return mfcc.MfccCompute(audioDataWindow);
+                                                                   });
         }
         return mfccFeatureCalc;
     }
diff --git a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
index 80e4a26..ae9e57a 100644
--- a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
@@ -27,8 +27,8 @@
     bool Wav2LetterMFCC::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&              filterBankFilterFirst,
+            std::vector<uint32_t>&              filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -41,11 +41,14 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
-            float melEnergy = 1e-10;  /* Avoid log of zero at later stages, same value used in librosa. */
-            const int32_t firstIndex = filterBankFilterFirst[bin];
-            const int32_t lastIndex = filterBankFilterLast[bin];
+            auto end = melFilterBank[bin].end();
+            /* Avoid log of zero at later stages, same value used in librosa.
+             * The number was used during our default wav2letter model training. */
+            float melEnergy = 1e-10;
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
                 melEnergy += (*filterBankIter++ * fftVec[i]);
             }
 
@@ -73,7 +76,7 @@
 
         /* Scale the log values and get the max. */
         for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
-                  iterM != melEnergies.end(); ++iterM, ++iterL) {
+                  iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
 
             *iterM = *iterL * multiplier;
 
@@ -86,8 +89,8 @@
         /* Clamp the mel energies. */
         constexpr float maxDb = 80.0;
         const float clampLevelLowdB = maxMelEnergy - maxDb;
-        for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) {
-            *iter = std::max(*iter, clampLevelLowdB);
+        for (float & melEnergie : melEnergies) {
+            melEnergie = std::max(melEnergie, clampLevelLowdB);
         }
     }
 
diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
index b173968..ee3aba0 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
@@ -38,13 +38,13 @@
                             const bool      lastIteration)
     {
         /* Basic checks. */
-        if (!this->_IsInputValid(tensor, axisIdx)) {
+        if (!this->IsInputValid(tensor, axisIdx)) {
             return false;
         }
 
         /* Irrespective of tensor type, we use unsigned "byte" */
         uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor);
-        const uint32_t elemSz = this->_GetTensorElementSize(tensor);
+        const uint32_t elemSz = this->GetTensorElementSize(tensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
@@ -58,9 +58,10 @@
         /* Which axis do we need to process? */
         switch (axisIdx) {
             case arm::app::Wav2LetterModel::ms_outputRowsIdx:
-                return this->_EraseSectionsRowWise(ptrData,
-                        elemSz * tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
-                        lastIteration);
+                return this->EraseSectionsRowWise(ptrData,
+                                                  elemSz *
+                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
+                                                  lastIteration);
             default:
                 printf_err("Unsupported axis index: %u\n", axisIdx);
         }
@@ -68,8 +69,8 @@
         return false;
     }
 
-    bool Postprocess::_IsInputValid(TfLiteTensor*  tensor,
-                                    const uint32_t axisIdx) const
+    bool Postprocess::IsInputValid(TfLiteTensor*  tensor,
+                                   const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -91,7 +92,7 @@
         return true;
     }
 
-    uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor*  tensor)
+    uint32_t Postprocess::GetTensorElementSize(TfLiteTensor*  tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
@@ -112,7 +113,7 @@
         return 0;
     }
 
-    bool Postprocess::_EraseSectionsRowWise(
+    bool Postprocess::EraseSectionsRowWise(
                         uint8_t*         ptrData,
                         const uint32_t   strideSzBytes,
                         const bool       lastIteration)
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
index 613ddb0..8251396 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
@@ -88,12 +88,12 @@
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        this->_ComputeDeltas(this->_m_mfccBuf,
-                             this->_m_delta1Buf,
-                             this->_m_delta2Buf);
+        this->ComputeDeltas(this->_m_mfccBuf,
+                            this->_m_delta1Buf,
+                            this->_m_delta2Buf);
 
         /* Normalise. */
-        this->_Normalise();
+        this->Normalise();
 
         /* Quantise. */
         QuantParams quantParams = GetTensorQuantParams(tensor);
@@ -105,11 +105,11 @@
 
         switch(tensor->type) {
             case kTfLiteUInt8:
-                return this->_Quantise<uint8_t>(
+                return this->Quantise<uint8_t>(
                         tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             case kTfLiteInt8:
-                return this->_Quantise<int8_t>(
+                return this->Quantise<int8_t>(
                         tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             default:
@@ -120,9 +120,9 @@
         return false;
     }
 
-    bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
-                                    Array2d<float>& delta1,
-                                    Array2d<float>& delta2)
+    bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+                                   Array2d<float>& delta1,
+                                   Array2d<float>& delta2)
     {
         const std::vector <float> delta1Coeffs =
             {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
@@ -175,20 +175,20 @@
         return true;
     }
 
-    float Preprocess::_GetMean(Array2d<float>& vec)
+    float Preprocess::GetMean(Array2d<float>& vec)
     {
         return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
     }
 
-    float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+    float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
     {
         return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
     }
 
-    void Preprocess::_NormaliseVec(Array2d<float>& vec)
+    void Preprocess::NormaliseVec(Array2d<float>& vec)
     {
-        auto mean = Preprocess::_GetMean(vec);
-        auto stddev = Preprocess::_GetStdDev(vec, mean);
+        auto mean = Preprocess::GetMean(vec);
+        auto stddev = Preprocess::GetStdDev(vec, mean);
 
         debug("Mean: %f, Stddev: %f\n", mean, stddev);
         if (stddev == 0) {
@@ -204,14 +204,14 @@
         }
     }
 
-    void Preprocess::_Normalise()
+    void Preprocess::Normalise()
     {
-        Preprocess::_NormaliseVec(this->_m_mfccBuf);
-        Preprocess::_NormaliseVec(this->_m_delta1Buf);
-        Preprocess::_NormaliseVec(this->_m_delta2Buf);
+        Preprocess::NormaliseVec(this->_m_mfccBuf);
+        Preprocess::NormaliseVec(this->_m_delta1Buf);
+        Preprocess::NormaliseVec(this->_m_delta2Buf);
     }
 
-    float Preprocess::_GetQuantElem(
+    float Preprocess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,
diff --git a/tests/common/ClassifierTests.cc b/tests/common/ClassifierTests.cc
index f08a09a..a04e4c2 100644
--- a/tests/common/ClassifierTests.cc
+++ b/tests/common/ClassifierTests.cc
@@ -18,6 +18,31 @@
 
 #include <catch.hpp>
 
+
+template<typename T>
+void test_classifier_result(std::vector<std::pair<uint32_t, T>>& selectedResults, T defaultTensorValue) {
+    const int dimArray[] = {1, 1001};
+    std::vector <std::string> labels(1001);
+    std::vector<T> outputVec(1001, defaultTensorValue);
+    TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
+    TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(outputVec.data(), dims, 1, 0);
+    TfLiteTensor* outputTensor = &tfTensor;
+
+    std::vector <arm::app::ClassificationResult> resultVec;
+
+    for (auto& selectedResult : selectedResults) {
+        outputVec[selectedResult.first] = selectedResult.second;
+    }
+
+    arm::app::Classifier classifier;
+    REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
+    REQUIRE(5 == resultVec.size());
+
+    for (size_t i = 0; i < resultVec.size(); ++i) {
+        REQUIRE(resultVec[i].m_labelIdx == selectedResults[i].first);
+    }
+}
+
 TEST_CASE("Common classifier")
 {
     SECTION("Test invalid classifier")
@@ -28,49 +53,31 @@
         REQUIRE(!classifier.GetClassificationResults(outputTens, resultVec, {}, 5));
     }
 
-    SECTION("Test valid classifier UINT8")
+    SECTION("Test classification results")
     {
-        const int dimArray[] = {1, 1001};
-        std::vector <std::string> labels(1001);
-        std::vector <uint8_t> outputVec(1001);
-        TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
-        TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
-                outputVec.data(), dims, 1, 0, "test");
-        TfLiteTensor* outputTensor = &tfTensor;
-        std::vector <arm::app::ClassificationResult> resultVec;
-        arm::app::Classifier classifier;
-        REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
-        REQUIRE(5 == resultVec.size());
-    }
+        SECTION("uint8") {
+            /* Set the top five results <position, score>. */
+            std::vector<std::pair<uint32_t, uint8_t>> selectedResults {
+                    {1000, 10}, {15, 9}, {0, 8}, {20, 7}, {10, 7} };
 
-    SECTION("Get classification results")
-    {
-        const int dimArray[] = {1, 1001};
-        std::vector <std::string> labels(1001);
-        std::vector<uint8_t> outputVec(1001, static_cast<uint8_t>(5));
-        TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
-        TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
-                outputVec.data(), dims, 1, 0, "test");
-        TfLiteTensor* outputTensor = &tfTensor;
-
-        std::vector <arm::app::ClassificationResult> resultVec;
-
-        /* Set the top five results. */
-        std::vector<std::pair<uint32_t, uint8_t>> selectedResults {
-                {0, 8}, {20, 7}, {10, 7}, {15, 9}, {1000, 10}};
-
-        for (size_t i = 0; i < selectedResults.size(); ++i) {
-            outputVec[selectedResults[i].first] = selectedResults[i].second;
+            test_classifier_result(selectedResults, static_cast<uint8_t>(5));
         }
 
-        arm::app::Classifier classifier;
-        REQUIRE(classifier.GetClassificationResults(outputTensor, resultVec, labels, 5));
-        REQUIRE(5 == resultVec.size());
+        SECTION("int8") {
+            /* Set the top five results <position, score>. */
+            std::vector<std::pair<uint32_t, int8_t>> selectedResults {
+                    {1000, 10}, {15, 9}, {0, 8}, {20, -7}, {10, -7} };
 
-        REQUIRE(resultVec[0].m_labelIdx == 1000);
-        REQUIRE(resultVec[1].m_labelIdx == 15);
-        REQUIRE(resultVec[2].m_labelIdx == 0);
-        REQUIRE(resultVec[3].m_labelIdx == 20);
-        REQUIRE(resultVec[4].m_labelIdx == 10);
+            test_classifier_result(selectedResults, static_cast<int8_t>(-100));
+        }
+
+        SECTION("float") {
+            /* Set the top five results <position, score>. */
+            std::vector<std::pair<uint32_t, float>> selectedResults {
+                    {1000, 10.9f}, {15, 9.8f}, {0, 8.7f}, {20, -7.0f}, {10, -7.1f} };
+
+            test_classifier_result(selectedResults, -100.0f);
+        }
+
     }
 }
diff --git a/tests/use_case/asr/AsrClassifierTests.cc b/tests/use_case/asr/AsrClassifierTests.cc
index 7c71912..12523aa 100644
--- a/tests/use_case/asr/AsrClassifierTests.cc
+++ b/tests/use_case/asr/AsrClassifierTests.cc
@@ -35,7 +35,7 @@
     std::vector <uint8_t> outputVec(7134);
     TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
     TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
-                                outputVec.data(), dims, 1, 0, "test");
+                                outputVec.data(), dims, 1, 0);
     TfLiteTensor* outputTensor = &tfTensor;
     std::vector <arm::app::ClassificationResult> resultVec;
     arm::app::AsrClassifier classifier;
@@ -51,7 +51,7 @@
     std::vector<uint8_t> outputVec(150, static_cast<uint8_t>(1));
     TfLiteIntArray* dims= tflite::testing::IntArrayFromInts(dimArray);
     TfLiteTensor tfTensor = tflite::testing::CreateQuantizedTensor(
-                                outputVec.data(), dims, 1, 0, "test");
+                                outputVec.data(), dims, 1, 0);
     TfLiteTensor* outputTensor = &tfTensor;
 
     std::vector <arm::app::ClassificationResult> resultVec(10);
diff --git a/tests/use_case/asr/AsrFeaturesTests.cc b/tests/use_case/asr/AsrFeaturesTests.cc
index 9401f40..59fe29b 100644
--- a/tests/use_case/asr/AsrFeaturesTests.cc
+++ b/tests/use_case/asr/AsrFeaturesTests.cc
@@ -25,30 +25,27 @@
 
 class TestPreprocess : public arm::app::audio::asr::Preprocess {
 public:
-    TestPreprocess()
-    : arm::app::audio::asr::Preprocess(0,0,0,0)
-    {}
 
-    bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
+    static bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
                        arm::app::Array2d<float>& delta1,
                        arm::app::Array2d<float>& delta2)
     {
-        return this->_ComputeDeltas(mfcc, delta1, delta2);
+        return Preprocess::ComputeDeltas(mfcc, delta1, delta2);
     }
 
-    float GetMean(arm::app::Array2d<float>& vec)
+    static float GetMean(arm::app::Array2d<float>& vec)
     {
-        return this->_GetMean(vec);
+        return Preprocess::GetMean(vec);
     }
 
-    float GetStdDev(arm::app::Array2d<float>& vec, const float mean)
+    static float GetStdDev(arm::app::Array2d<float>& vec, const float mean)
     {
-       return this->_GetStdDev(vec, mean);
+       return Preprocess::GetStdDev(vec, mean);
     }
 
-    void NormaliseVec(arm::app::Array2d<float>& vec)
+    static void NormaliseVec(arm::app::Array2d<float>& vec)
     {
-        return this->_NormaliseVec(vec);
+        return Preprocess::NormaliseVec(vec);
     }
 };
 
@@ -86,7 +83,6 @@
 
 TEST_CASE("Floating point asr features calculation", "[ASR]")
 {
-    TestPreprocess tp;
 
     SECTION("First and second diff")
     {
@@ -109,7 +105,7 @@
         std::fill(delta1Buf.begin(), delta1Buf.end(), 0.f);
         std::fill(delta2Buf.begin(), delta2Buf.end(), 0.f);
 
-        tp.ComputeDeltas(mfccBuf, delta1Buf, delta2Buf);
+        TestPreprocess::ComputeDeltas(mfccBuf, delta1Buf, delta2Buf);
 
         /* First 4 and last 4 values are different because we pad AFTER diff calculated. */
         for (size_t i = 0; i < numMfccFeats; ++i) {
@@ -136,37 +132,37 @@
                                                 {-1, -2}};
         arm::app::Array2d<float> mean1(2,2); /* {{1, 2},{-1, -2}} */
         populateArray2dWithVectorOfVector(mean1vec, mean1);
-        REQUIRE(0 == Approx(tp.GetMean(mean1)));
+        REQUIRE(0 == Approx(TestPreprocess::GetMean(mean1)));
 
         arm::app::Array2d<float> mean2(2, 2);
         std::fill(mean2.begin(), mean2.end(), 0.f);
-        REQUIRE(0 == Approx(tp.GetMean(mean2)));
+        REQUIRE(0 == Approx(TestPreprocess::GetMean(mean2)));
 
         arm::app::Array2d<float> mean3(3,3);
         std::fill(mean3.begin(), mean3.end(), 1.f);
-        REQUIRE(1 == Approx(tp.GetMean(mean3)));
+        REQUIRE(1 == Approx(TestPreprocess::GetMean(mean3)));
     }
 
     SECTION("Std")
     {
         arm::app::Array2d<float> std1(2, 2);
         std::fill(std1.begin(), std1.end(), 0.f); /* {{0, 0}, {0, 0}} */
-        REQUIRE(0 == Approx(tp.GetStdDev(std1, 0)));
+        REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std1, 0)));
 
         std::vector<std::vector<float>> std2vec{{1, 2, 3, 4, 5}, {6, 7, 8, 9, 0}};
         arm::app::Array2d<float> std2(2,5);
         populateArray2dWithVectorOfVector(std2vec, std2);
-        const float mean = tp.GetMean(std2);
-        REQUIRE(2.872281323 == Approx(tp.GetStdDev(std2, mean)));
+        const float mean = TestPreprocess::GetMean(std2);
+        REQUIRE(2.872281323 == Approx(TestPreprocess::GetStdDev(std2, mean)));
 
         arm::app::Array2d<float> std3(2,2);
         std::fill(std3.begin(), std3.end(), 1.f); /* std3{{1, 1}, {1, 1}}; */
-        REQUIRE(0 == Approx(tp.GetStdDev(std3, 1)));
+        REQUIRE(0 == Approx(TestPreprocess::GetStdDev(std3, 1)));
     }
 
     SECTION("Norm") {
         auto checker = [&](arm::app::Array2d<float>& d, std::vector<float>& g) {
-            tp.NormaliseVec(d);
+            TestPreprocess::NormaliseVec(d);
             std::vector<float> d_vec(d.begin(), d.end());
             REQUIRE_THAT(g, Catch::Approx(d_vec));
         };