diff --git a/source/use_case/kws/include/KwsProcessing.hpp b/source/use_case/kws/include/KwsProcessing.hpp
new file mode 100644
index 0000000..abf20ab
--- /dev/null
+++ b/source/use_case/kws/include/KwsProcessing.hpp
@@ -0,0 +1,135 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#ifndef KWS_PROCESSING_HPP
+#define KWS_PROCESSING_HPP
+
+#include <AudioUtils.hpp>
+#include "BaseProcessing.hpp"
+#include "Model.hpp"
+#include "Classifier.hpp"
+#include "MicroNetKwsMfcc.hpp"
+
+#include <functional>
+
+namespace arm {
+namespace app {
+
+    /**
+     * @brief   Pre-processing class for Keyword Spotting use case.
+     *          Implements methods declared by BasePreProcess and anything else needed
+     *          to populate input tensors ready for inference.
+     */
+    class KWSPreProcess : public BasePreProcess {
+
+    public:
+        /**
+         * @brief       Constructor
+         * @param[in]   model             Pointer to the the KWS Model object.
+         * @param[in]   numFeatures       How many MFCC features to use.
+         * @param[in]   mfccFrameLength   Number of audio samples used to calculate one set of MFCC values when
+         *                                sliding a window through the audio sample.
+         * @param[in]   mfccFrameStride   Number of audio samples between consecutive windows.
+         **/
+        explicit KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride);
+
+        /**
+         * @brief       Should perform pre-processing of 'raw' input audio data and load it into
+         *              TFLite Micro input tensors ready for inference.
+         * @param[in]   input      Pointer to the data that pre-processing will work on.
+         * @param[in]   inputSize  Size of the input data.
+         * @return      true if successful, false otherwise.
+         **/
+        bool DoPreProcess(const void* input, size_t inputSize) override;
+
+        size_t m_audioWindowIndex = 0;  /* Index of audio slider, used when caching features in longer clips. */
+        size_t m_audioDataWindowSize;   /* Amount of audio needed for 1 inference. */
+        size_t m_audioDataStride;       /* Amount of audio to stride across if doing >1 inference in longer clips. */
+
+    private:
+        const int m_mfccFrameLength;
+        const int m_mfccFrameStride;
+
+        audio::MicroNetKwsMFCC m_mfcc;
+        audio::SlidingWindow<const int16_t> m_mfccSlidingWindow;
+        size_t m_numMfccVectorsInAudioStride;
+        size_t m_numReusedMfccVectors;
+        std::function<void (std::vector<int16_t>&, int, bool, size_t)> m_mfccFeatureCalculator;
+
+        /**
+         * @brief Returns a function to perform feature calculation and populates input tensor data with
+         * MFCC data.
+         *
+         * Input tensor data type check is performed to choose correct MFCC feature data type.
+         * If tensor has an integer data type then original features are quantised.
+         *
+         * Warning: MFCC calculator provided as input must have the same life scope as returned function.
+         *
+         * @param[in]       mfcc          MFCC feature calculator.
+         * @param[in,out]   inputTensor   Input tensor pointer to store calculated features.
+         * @param[in]       cacheSize     Size of the feature vectors cache (number of feature vectors).
+         * @return          Function to be called providing audio sample and sliding window index.
+         */
+        std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+        GetFeatureCalculator(audio::MicroNetKwsMFCC&  mfcc,
+                             TfLiteTensor*            inputTensor,
+                             size_t                   cacheSize);
+
+        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);
+    };
+
+    /**
+     * @brief   Post-processing class for Keyword Spotting use case.
+     *          Implements methods declared by BasePostProcess and anything else needed
+     *          to populate result vector.
+     */
+    class KWSPostProcess : public BasePostProcess {
+
+    private:
+        Classifier& m_kwsClassifier;
+        const std::vector<std::string>& m_labels;
+        std::vector<ClassificationResult>& m_results;
+
+    public:
+        const float m_scoreThreshold;
+        /**
+         * @brief       Constructor
+         * @param[in]   classifier       Classifier object used to get top N results from classification.
+         * @param[in]   model            Pointer to the the Image classification Model object.
+         * @param[in]   labels           Vector of string labels to identify each output of the model.
+         * @param[in]   results          Vector of classification results to store decoded outputs.
+         * @param[in]   scoreThreshold   Predicted model score must be larger than this value to be accepted.
+         **/
+        KWSPostProcess(Classifier& classifier, Model* model,
+                       const std::vector<std::string>& labels,
+                       std::vector<ClassificationResult>& results,
+                       float scoreThreshold);
+
+        /**
+         * @brief       Should perform post-processing of the result of inference then populate
+         *              populate KWS result data for any later use.
+         * @return      true if successful, false otherwise.
+         **/
+        bool DoPostProcess() override;
+    };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* KWS_PROCESSING_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc
new file mode 100644
index 0000000..b6b230c
--- /dev/null
+++ b/source/use_case/kws/src/KwsProcessing.cc
@@ -0,0 +1,220 @@
+/*
+ * Copyright (c) 2022 Arm Limited. All rights reserved.
+ * SPDX-License-Identifier: Apache-2.0
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "KwsProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "log_macros.h"
+#include "MicroNetKwsModel.hpp"
+
+namespace arm {
+namespace app {
+
+    KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride):
+        m_mfccFrameLength{mfccFrameLength},
+        m_mfccFrameStride{mfccFrameStride},
+        m_mfcc{audio::MicroNetKwsMFCC(numFeatures, mfccFrameLength)}
+    {
+        if (!model->IsInited()) {
+            printf_err("Model is not initialised!.\n");
+        }
+        this->m_model = model;
+        this->m_mfcc.Init();
+
+        TfLiteIntArray* inputShape = model->GetInputShape(0);
+        const uint32_t numMfccFrames = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
+
+        /* Deduce the data length required for 1 inference from the network parameters. */
+        this->m_audioDataWindowSize = numMfccFrames * this->m_mfccFrameStride +
+                (this->m_mfccFrameLength - this->m_mfccFrameStride);
+
+        /* Creating an MFCC feature sliding window for the data required for 1 inference. */
+        this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize,
+                this->m_mfccFrameLength, this->m_mfccFrameStride);
+
+        /* For longer audio clips we choose to move by half the audio window size
+         * => for a 1 second window size there is an overlap of 0.5 seconds. */
+        this->m_audioDataStride = this->m_audioDataWindowSize / 2;
+
+        /* To have the previously calculated features re-usable, stride must be multiple
+         * of MFCC features window stride. Reduce stride through audio if needed. */
+        if (0 != this->m_audioDataStride % this->m_mfccFrameStride) {
+            this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride;
+        }
+
+        this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride;
+
+        /* Calculate number of the feature vectors in the window overlap region.
+         * These feature vectors will be reused.*/
+        this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1
+                - this->m_numMfccVectorsInAudioStride;
+
+        /* Construct feature calculation function. */
+        this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0),
+                                                             this->m_numReusedMfccVectors);
+
+        if (!this->m_mfccFeatureCalculator) {
+            printf_err("Feature calculator not initialized.");
+        }
+    }
+
+    bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize)
+    {
+        UNUSED(inputSize);
+        if (data == nullptr) {
+            printf_err("Data pointer is null");
+        }
+
+        /* Set the features sliding window to the new address. */
+        auto input = static_cast<const int16_t*>(data);
+        this->m_mfccSlidingWindow.Reset(input);
+
+        /* Cache is only usable if we have more than 1 inference in an audio clip. */
+        bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0;
+
+        /* Use a sliding window to calculate MFCC features frame by frame. */
+        while (this->m_mfccSlidingWindow.HasNext()) {
+            const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
+
+            std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow,
+                    mfccWindow + this->m_mfccFrameLength);
+
+            /* Compute features for this window and write them to input tensor. */
+            this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(),
+                                          useCache, this->m_numMfccVectorsInAudioStride);
+        }
+
+        debug("Input tensor populated \n");
+
+        return true;
+    }
+
+    /**
+     * @brief Generic feature calculator factory.
+     *
+     * Returns lambda function to compute features using features cache.
+     * Real features math is done by a lambda function provided as a parameter.
+     * Features are written to input tensor memory.
+     *
+     * @tparam T                Feature vector type.
+     * @param[in] inputTensor   Model input tensor pointer.
+     * @param[in] cacheSize     Number of feature vectors to cache. Defined by the sliding window overlap.
+     * @param[in] compute       Features calculator function.
+     * @return                  Lambda function to compute features.
+     */
+    template<class T>
+    std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
+    KWSPreProcess::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);
+
+        return [=](std::vector<int16_t>& audioDataWindow,
+                   size_t index,
+                   bool useCache,
+                   size_t featuresOverlapIndex)
+        {
+            T* tensorData = tflite::GetTensorData<T>(inputTensor);
+            std::vector<T> features;
+
+            /* Reuse features from cache if cache is ready and sliding windows overlap.
+             * Overlap is in the beginning of sliding window with a size of a feature cache. */
+            if (useCache && index < featureCache.size()) {
+                features = std::move(featureCache[index]);
+            } else {
+                features = std::move(compute(audioDataWindow));
+            }
+            auto size = features.size();
+            auto sizeBytes = sizeof(T) * size;
+            std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
+
+            /* Start renewing cache as soon iteration goes out of the windows overlap. */
+            if (index >= featuresOverlapIndex) {
+                featureCache[index - featuresOverlapIndex] = std::move(features);
+            }
+        };
+    }
+
+    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+    KWSPreProcess::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)>
+    KWSPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
+                       size_t cacheSize,
+                       std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+
+
+    std::function<void (std::vector<int16_t>&, int, bool, size_t)>
+    KWSPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
+    {
+        std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
+
+        TfLiteQuantization quant = inputTensor->quantization;
+
+        if (kTfLiteAffineQuantization == quant.type) {
+            auto *quantParams = (TfLiteAffineQuantization *) quant.params;
+            const float quantScale = quantParams->scale->data[0];
+            const int quantOffset = quantParams->zero_point->data[0];
+
+            switch (inputTensor->type) {
+                case kTfLiteInt8: {
+                    mfccFeatureCalc = this->FeatureCalc<int8_t>(inputTensor,
+                                                          cacheSize,
+                                                          [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                              return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+                                                                                                   quantScale,
+                                                                                                   quantOffset);
+                                                          }
+                    );
+                    break;
+                }
+                default:
+                printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
+            }
+        } else {
+            mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize,
+                    [&mfcc](std::vector<int16_t>& audioDataWindow) {
+                return mfcc.MfccCompute(audioDataWindow); }
+                );
+        }
+        return mfccFeatureCalc;
+    }
+
+    KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model,
+                                   const std::vector<std::string>& labels,
+                                   std::vector<ClassificationResult>& results, float scoreThreshold)
+            :m_kwsClassifier{classifier},
+             m_labels{labels},
+             m_results{results},
+             m_scoreThreshold{scoreThreshold}
+    {
+        if (!model->IsInited()) {
+            printf_err("Model is not initialised!.\n");
+        }
+        this->m_model = model;
+    }
+
+    bool KWSPostProcess::DoPostProcess()
+    {
+        return this->m_kwsClassifier.GetClassificationResults(
+                this->m_model->GetOutputTensor(0), this->m_results,
+                this->m_labels, 1, true);
+    }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index e04cefc..350d34b 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -20,15 +20,14 @@
 #include "Classifier.hpp"
 #include "MicroNetKwsModel.hpp"
 #include "hal.h"
-#include "MicroNetKwsMfcc.hpp"
 #include "AudioUtils.hpp"
 #include "ImageUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
 #include "KwsResult.hpp"
 #include "log_macros.h"
+#include "KwsProcessing.hpp"
 
 #include <vector>
-#include <functional>
 
 using KwsClassifier = arm::app::Classifier;
 
@@ -37,36 +36,27 @@
 
 
     /**
-     * @brief           Presents inference results using the data presentation
-     *                  object.
-     * @param[in]       results     Vector of classification results to be displayed.
+     * @brief           Presents KWS inference results.
+     * @param[in]       results     Vector of KWS classification results to be displayed.
      * @return          true if successful, false otherwise.
      **/
     static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results);
 
-    /**
-     * @brief Returns a function to perform feature calculation and populates input tensor data with
-     * MFCC data.
-     *
-     * Input tensor data type check is performed to choose correct MFCC feature data type.
-     * If tensor has an integer data type then original features are quantised.
-     *
-     * Warning: MFCC calculator provided as input must have the same life scope as returned function.
-     *
-     * @param[in]       mfcc          MFCC feature calculator.
-     * @param[in,out]   inputTensor   Input tensor pointer to store calculated features.
-     * @param[in]       cacheSize     Size of the feature vectors cache (number of feature vectors).
-     * @return          Function to be called providing audio sample and sliding window index.
-     */
-    static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-            GetFeatureCalculator(audio::MicroNetKwsMFCC&  mfcc,
-                                 TfLiteTensor*      inputTensor,
-                                 size_t             cacheSize);
-
-    /* Audio inference handler. */
+    /* KWS inference handler. */
     bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
     {
         auto& profiler = ctx.Get<Profiler&>("profiler");
+        auto& model = ctx.Get<Model&>("model");
+        const auto mfccFrameLength = ctx.Get<int>("frameLength");
+        const auto mfccFrameStride = ctx.Get<int>("frameStride");
+        const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+        /* If the request has a valid size, set the audio index. */
+        if (clipIndex < NUMBER_OF_FILES) {
+            if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) {
+                return false;
+            }
+        }
+        auto initialClipIdx = ctx.Get<uint32_t>("clipIndex");
 
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -74,27 +64,13 @@
             (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
              arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
 
-        auto& model = ctx.Get<Model&>("model");
 
-        /* If the request has a valid size, set the audio index. */
-        if (clipIndex < NUMBER_OF_FILES) {
-            if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) {
-                return false;
-            }
-        }
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
         }
 
-        const auto frameLength = ctx.Get<int>("frameLength");
-        const auto frameStride = ctx.Get<int>("frameStride");
-        const auto scoreThreshold = ctx.Get<float>("scoreThreshold");
-        auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
-
-        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
-
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -103,130 +79,89 @@
             return false;
         }
 
+        /* Get input shape for feature extraction. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-        const uint32_t kNumCols = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
-        const uint32_t kNumRows = inputShape->data[arm::app::MicroNetKwsModel::ms_inputRowsIdx];
-
-        audio::MicroNetKwsMFCC mfcc = audio::MicroNetKwsMFCC(kNumCols, frameLength);
-        mfcc.Init();
-
-        /* Deduce the data length required for 1 inference from the network parameters. */
-        auto audioDataWindowSize = kNumRows * frameStride + (frameLength - frameStride);
-        auto mfccWindowSize = frameLength;
-        auto mfccWindowStride = frameStride;
-
-        /* We choose to move by half the window size => for a 1 second window size
-         * there is an overlap of 0.5 seconds. */
-        auto audioDataStride = audioDataWindowSize / 2;
-
-        /* To have the previously calculated features re-usable, stride must be multiple
-         * of MFCC features window stride. */
-        if (0 != audioDataStride % mfccWindowStride) {
-
-            /* Reduce the stride. */
-            audioDataStride -= audioDataStride % mfccWindowStride;
-        }
-
-        auto nMfccVectorsInAudioStride = audioDataStride/mfccWindowStride;
+        const uint32_t numMfccFeatures = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
 
         /* We expect to be sampling 1 second worth of data at a time.
          * NOTE: This is only used for time stamp calculation. */
-        const float secondsPerSample = 1.0/audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
+        const float secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
+
+        /* Set up pre and post-processing. */
+        KWSPreProcess preprocess = KWSPreProcess(&model, numMfccFeatures, mfccFrameLength, mfccFrameStride);
+
+        std::vector<ClassificationResult> singleInfResult;
+        KWSPostProcess postprocess = KWSPostProcess(ctx.Get<KwsClassifier &>("classifier"), &model,
+                                                    ctx.Get<std::vector<std::string>&>("labels"),
+                                                    singleInfResult, scoreThreshold);
+
+        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
 
         do {
             hal_lcd_clear(COLOR_BLACK);
 
             auto currentIndex = ctx.Get<uint32_t>("clipIndex");
 
-            /* Creating a mfcc features sliding window for the data required for 1 inference. */
-            auto audioMFCCWindowSlider = audio::SlidingWindow<const int16_t>(
-                                            get_audio_array(currentIndex),
-                                            audioDataWindowSize, mfccWindowSize,
-                                            mfccWindowStride);
-
             /* Creating a sliding window through the whole audio clip. */
             auto audioDataSlider = audio::SlidingWindow<const int16_t>(
-                                        get_audio_array(currentIndex),
-                                        get_audio_array_size(currentIndex),
-                                        audioDataWindowSize, audioDataStride);
+                    get_audio_array(currentIndex),
+                    get_audio_array_size(currentIndex),
+                    preprocess.m_audioDataWindowSize, preprocess.m_audioDataStride);
 
-            /* Calculate number of the feature vectors in the window overlap region.
-             * These feature vectors will be reused.*/
-            auto numberOfReusedFeatureVectors = audioMFCCWindowSlider.TotalStrides() + 1
-                                                - nMfccVectorsInAudioStride;
-
-            /* Construct feature calculation function. */
-            auto mfccFeatureCalc = GetFeatureCalculator(mfcc, inputTensor,
-                                                        numberOfReusedFeatureVectors);
-
-            if (!mfccFeatureCalc){
-                return false;
-            }
-
-            /* Declare a container for results. */
-            std::vector<arm::app::kws::KwsResult> results;
+            /* Declare a container to hold results from across the whole audio clip. */
+            std::vector<kws::KwsResult> finalResults;
 
             /* Display message on the LCD - inference running. */
             std::string str_inf{"Running inference... "};
-            hal_lcd_display_text(
-                                str_inf.c_str(), str_inf.size(),
-                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+            hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
             info("Running inference on audio clip %" PRIu32 " => %s\n", currentIndex,
                  get_filename(currentIndex));
 
             /* Start sliding through audio clip. */
             while (audioDataSlider.HasNext()) {
-                const int16_t *inferenceWindow = audioDataSlider.Next();
-
-                /* We moved to the next window - set the features sliding to the new address. */
-                audioMFCCWindowSlider.Reset(inferenceWindow);
+                const int16_t* inferenceWindow = audioDataSlider.Next();
 
                 /* The first window does not have cache ready. */
-                bool useCache = audioDataSlider.Index() > 0 && numberOfReusedFeatureVectors > 0;
-
-                /* Start calculating features inside one audio sliding window. */
-                while (audioMFCCWindowSlider.HasNext()) {
-                    const int16_t *mfccWindow = audioMFCCWindowSlider.Next();
-                    std::vector<int16_t> mfccAudioData = std::vector<int16_t>(mfccWindow,
-                                                            mfccWindow + mfccWindowSize);
-                    /* Compute features for this window and write them to input tensor. */
-                    mfccFeatureCalc(mfccAudioData,
-                                    audioMFCCWindowSlider.Index(),
-                                    useCache,
-                                    nMfccVectorsInAudioStride);
-                }
+                preprocess.m_audioWindowIndex = audioDataSlider.Index();
 
                 info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                      audioDataSlider.TotalStrides() + 1);
 
-                /* Run inference over this audio clip sliding window. */
-                if (!RunInference(model, profiler)) {
+                /* Run the pre-processing, inference and post-processing. */
+                if (!runner.PreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
                     return false;
                 }
 
-                std::vector<ClassificationResult> classificationResult;
-                auto& classifier = ctx.Get<KwsClassifier&>("classifier");
-                classifier.GetClassificationResults(outputTensor, classificationResult,
-                                                    ctx.Get<std::vector<std::string>&>("labels"), 1, true);
+                profiler.StartProfiling("Inference");
+                if (!runner.RunInference()) {
+                    return false;
+                }
+                profiler.StopProfiling();
 
-                results.emplace_back(kws::KwsResult(classificationResult,
-                    audioDataSlider.Index() * secondsPerSample * audioDataStride,
-                    audioDataSlider.Index(), scoreThreshold));
+                if (!runner.PostProcess()) {
+                    return false;
+                }
+
+                /* Add results from this window to our final results vector. */
+                finalResults.emplace_back(kws::KwsResult(singleInfResult,
+                        audioDataSlider.Index() * secondsPerSample * preprocess.m_audioDataStride,
+                        audioDataSlider.Index(), postprocess.m_scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
+                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
                 arm::app::DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
             } /* while (audioDataSlider.HasNext()) */
 
             /* Erase. */
             str_inf = std::string(str_inf.size(), ' ');
-            hal_lcd_display_text(
-                                str_inf.c_str(), str_inf.size(),
-                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+            hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
-            ctx.Set<std::vector<arm::app::kws::KwsResult>>("results", results);
+            ctx.Set<std::vector<kws::KwsResult>>("results", finalResults);
 
-            if (!PresentInferenceResult(results)) {
+            if (!PresentInferenceResult(finalResults)) {
                 return false;
             }
 
@@ -234,58 +169,11 @@
 
             IncrementAppCtxIfmIdx(ctx,"clipIndex");
 
-        } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
+        } while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
 
         return true;
     }
 
-    /**
-     * @brief Generic feature calculator factory.
-     *
-     * Returns lambda function to compute features using features cache.
-     * Real features math is done by a lambda function provided as a parameter.
-     * Features are written to input tensor memory.
-     *
-     * @tparam T                Feature vector type.
-     * @param[in] inputTensor   Model input tensor pointer.
-     * @param[in] cacheSize     Number of feature vectors to cache. Defined by the sliding window overlap.
-     * @param[in] compute       Features calculator function.
-     * @return                  Lambda function to compute features.
-     */
-    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)
-    {
-        /* Feature cache to be captured by lambda function. */
-        static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
-
-        return [=](std::vector<int16_t>& audioDataWindow,
-                                     size_t index,
-                                     bool useCache,
-                                     size_t featuresOverlapIndex)
-        {
-            T *tensorData = tflite::GetTensorData<T>(inputTensor);
-            std::vector<T> features;
-
-            /* Reuse features from cache if cache is ready and sliding windows overlap.
-             * Overlap is in the beginning of sliding window with a size of a feature cache. */
-            if (useCache && index < featureCache.size()) {
-                features = std::move(featureCache[index]);
-            } else {
-                features = std::move(compute(audioDataWindow));
-            }
-            auto size = features.size();
-            auto sizeBytes = sizeof(T) * size;
-            std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
-
-            /* Start renewing cache as soon iteration goes out of the windows overlap. */
-            if (index >= featuresOverlapIndex) {
-                featureCache[index - featuresOverlapIndex] = std::move(features);
-            }
-        };
-    }
-
     static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
@@ -299,40 +187,39 @@
         /* Display each result */
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
 
-        for (uint32_t i = 0; i < results.size(); ++i) {
+        for (const auto & result : results) {
 
             std::string topKeyword{"<none>"};
             float score = 0.f;
-            if (!results[i].m_resultVec.empty()) {
-                topKeyword = results[i].m_resultVec[0].m_label;
-                score = results[i].m_resultVec[0].m_normalisedVal;
+            if (!result.m_resultVec.empty()) {
+                topKeyword = result.m_resultVec[0].m_label;
+                score = result.m_resultVec[0].m_normalisedVal;
             }
 
             std::string resultStr =
-                    std::string{"@"} + std::to_string(results[i].m_timeStamp) +
+                    std::string{"@"} + std::to_string(result.m_timeStamp) +
                     std::string{"s: "} + topKeyword + std::string{" ("} +
                     std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
 
-            hal_lcd_display_text(
-                    resultStr.c_str(), resultStr.size(),
+            hal_lcd_display_text(resultStr.c_str(), resultStr.size(),
                     dataPsnTxtStartX1, rowIdx1, false);
             rowIdx1 += dataPsnTxtYIncr;
 
-            if (results[i].m_resultVec.empty()) {
+            if (result.m_resultVec.empty()) {
                 info("For timestamp: %f (inference #: %" PRIu32
                              "); label: %s; threshold: %f\n",
-                     results[i].m_timeStamp, results[i].m_inferenceNumber,
+                     result.m_timeStamp, result.m_inferenceNumber,
                      topKeyword.c_str(),
-                     results[i].m_threshold);
+                     result.m_threshold);
             } else {
-                for (uint32_t j = 0; j < results[i].m_resultVec.size(); ++j) {
+                for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
                     info("For timestamp: %f (inference #: %" PRIu32
                                  "); label: %s, score: %f; threshold: %f\n",
-                         results[i].m_timeStamp,
-                         results[i].m_inferenceNumber,
-                         results[i].m_resultVec[j].m_label.c_str(),
-                         results[i].m_resultVec[j].m_normalisedVal,
-                         results[i].m_threshold);
+                         result.m_timeStamp,
+                         result.m_inferenceNumber,
+                         result.m_resultVec[j].m_label.c_str(),
+                         result.m_resultVec[j].m_normalisedVal,
+                         result.m_threshold);
                 }
             }
         }
@@ -340,88 +227,5 @@
         return true;
     }
 
-    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-        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);
-
-    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);
-
-    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);
-
-
-    static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
-    {
-        std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
-
-        TfLiteQuantization quant = inputTensor->quantization;
-
-        if (kTfLiteAffineQuantization == quant.type) {
-
-            auto *quantParams = (TfLiteAffineQuantization *) quant.params;
-            const float quantScale = quantParams->scale->data[0];
-            const int quantOffset = quantParams->zero_point->data[0];
-
-            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);
-                                                          }
-                    );
-                    break;
-                }
-                case kTfLiteUInt8: {
-                    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);
-                                                           }
-                    );
-                    break;
-                }
-                default:
-                    printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
-            }
-
-
-        } else {
-            mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
-                                                                   cacheSize,
-                                                                   [&mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                       return mfcc.MfccCompute(audioDataWindow);
-                                                                   });
-        }
-        return mfccFeatureCalc;
-    }
-
 } /* namespace app */
-} /* namespace arm */
\ No newline at end of file
+} /* namespace arm */