diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index e733605..8a024b7 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -1,6 +1,6 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2021-2022 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> 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.
@@ -16,35 +16,34 @@
  */
 #include "UseCaseHandler.hpp"
 
-#include "hal.h"
-#include "InputFiles.hpp"
-#include "AudioUtils.hpp"
-#include "ImageUtils.hpp"
-#include "UseCaseCommonUtils.hpp"
-#include "MicroNetKwsModel.hpp"
-#include "MicroNetKwsMfcc.hpp"
-#include "Classifier.hpp"
-#include "KwsResult.hpp"
-#include "Wav2LetterModel.hpp"
-#include "Wav2LetterMfcc.hpp"
-#include "Wav2LetterPreprocess.hpp"
-#include "Wav2LetterPostprocess.hpp"
-#include "KwsProcessing.hpp"
-#include "AsrResult.hpp"
 #include "AsrClassifier.hpp"
+#include "AsrResult.hpp"
+#include "AudioUtils.hpp"
+#include "Classifier.hpp"
+#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
+#include "KwsProcessing.hpp"
+#include "KwsResult.hpp"
+#include "MicroNetKwsMfcc.hpp"
+#include "MicroNetKwsModel.hpp"
 #include "OutputDecode.hpp"
+#include "UseCaseCommonUtils.hpp"
+#include "Wav2LetterMfcc.hpp"
+#include "Wav2LetterModel.hpp"
+#include "Wav2LetterPostprocess.hpp"
+#include "Wav2LetterPreprocess.hpp"
+#include "hal.h"
 #include "log_macros.h"
 
-
 using KwsClassifier = arm::app::Classifier;
 
 namespace arm {
 namespace app {
 
     struct KWSOutput {
-        bool executionSuccess = false;
+        bool executionSuccess        = false;
         const int16_t* asrAudioStart = nullptr;
-        int32_t asrAudioSamples = 0;
+        int32_t asrAudioSamples      = 0;
     };
 
     /**
@@ -69,23 +68,24 @@
      **/
     static KWSOutput doKws(ApplicationContext& ctx)
     {
-        auto& profiler = ctx.Get<Profiler&>("profiler");
-        auto& kwsModel = ctx.Get<Model&>("kwsModel");
+        auto& profiler                = ctx.Get<Profiler&>("profiler");
+        auto& kwsModel                = ctx.Get<Model&>("kwsModel");
         const auto kwsMfccFrameLength = ctx.Get<int>("kwsFrameLength");
         const auto kwsMfccFrameStride = ctx.Get<int>("kwsFrameStride");
-        const auto kwsScoreThreshold = ctx.Get<float>("kwsScoreThreshold");
+        const auto kwsScoreThreshold  = ctx.Get<float>("kwsScoreThreshold");
 
         auto currentIndex = ctx.Get<uint32_t>("clipIndex");
 
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
 
-        constexpr int minTensorDims = static_cast<int>(
-            (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)?
-             MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx);
+        constexpr int minTensorDims =
+            static_cast<int>((MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)
+                                 ? MicroNetKwsModel::ms_inputRowsIdx
+                                 : MicroNetKwsModel::ms_inputColsIdx);
 
         /* Output struct from doing KWS. */
-        KWSOutput output {};
+        KWSOutput output{};
 
         if (!kwsModel.IsInited()) {
             printf_err("KWS model has not been initialised\n");
@@ -93,7 +93,7 @@
         }
 
         /* Get Input and Output tensors for pre/post processing. */
-        TfLiteTensor* kwsInputTensor = kwsModel.GetInputTensor(0);
+        TfLiteTensor* kwsInputTensor  = kwsModel.GetInputTensor(0);
         TfLiteTensor* kwsOutputTensor = kwsModel.GetOutputTensor(0);
         if (!kwsInputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
@@ -104,28 +104,30 @@
         }
 
         /* Get input shape for feature extraction. */
-        TfLiteIntArray* inputShape = kwsModel.GetInputShape(0);
+        TfLiteIntArray* inputShape     = kwsModel.GetInputShape(0);
         const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
-        const uint32_t numMfccFrames = inputShape->data[MicroNetKwsModel::ms_inputRowsIdx];
+        const uint32_t numMfccFrames   = inputShape->data[MicroNetKwsModel::ms_inputRowsIdx];
 
         /* We expect to be sampling 1 second worth of data at a time
          * NOTE: This is only used for time stamp calculation. */
-        const float kwsAudioParamsSecondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
+        const float kwsAudioParamsSecondsPerSample =
+            1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
 
         /* Set up pre and post-processing. */
-        KwsPreProcess preProcess = KwsPreProcess(kwsInputTensor, numMfccFeatures, numMfccFrames,
-                                                 kwsMfccFrameLength, kwsMfccFrameStride);
+        KwsPreProcess preProcess = KwsPreProcess(
+            kwsInputTensor, numMfccFeatures, numMfccFrames, kwsMfccFrameLength, kwsMfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
-        KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor, ctx.Get<KwsClassifier &>("kwsClassifier"),
+        KwsPostProcess postProcess = KwsPostProcess(kwsOutputTensor,
+                                                    ctx.Get<KwsClassifier&>("kwsClassifier"),
                                                     ctx.Get<std::vector<std::string>&>("kwsLabels"),
                                                     singleInfResult);
 
         /* 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),
-                preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride);
+        auto audioDataSlider = audio::SlidingWindow<const int16_t>(GetAudioArray(currentIndex),
+                                                                   GetAudioArraySize(currentIndex),
+                                                                   preProcess.m_audioDataWindowSize,
+                                                                   preProcess.m_audioDataStride);
 
         /* Declare a container to hold kws results from across the whole audio clip. */
         std::vector<kws::KwsResult> finalResults;
@@ -133,11 +135,11 @@
         /* Display message on the LCD - inference running. */
         std::string str_inf{"Running KWS inference... "};
         hal_lcd_display_text(
-                            str_inf.c_str(), str_inf.size(),
-                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+            str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
         info("Running KWS inference on audio clip %" PRIu32 " => %s\n",
-             currentIndex, get_filename(currentIndex));
+             currentIndex,
+             GetFilename(currentIndex));
 
         /* Start sliding through audio clip. */
         while (audioDataSlider.HasNext()) {
@@ -159,22 +161,26 @@
                 return output;
             }
 
-            info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
+            info("Inference %zu/%zu\n",
+                 audioDataSlider.Index() + 1,
                  audioDataSlider.TotalStrides() + 1);
 
             /* Add results from this window to our final results vector. */
             finalResults.emplace_back(
-                    kws::KwsResult(singleInfResult,
-                            audioDataSlider.Index() * kwsAudioParamsSecondsPerSample * preProcess.m_audioDataStride,
-                            audioDataSlider.Index(), kwsScoreThreshold));
+                kws::KwsResult(singleInfResult,
+                               audioDataSlider.Index() * kwsAudioParamsSecondsPerSample *
+                                   preProcess.m_audioDataStride,
+                               audioDataSlider.Index(),
+                               kwsScoreThreshold));
 
             /* Break out when trigger keyword is detected. */
-            if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword")
-                    && singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
+            if (singleInfResult[0].m_label == ctx.Get<const std::string&>("triggerKeyword") &&
+                singleInfResult[0].m_normalisedVal > kwsScoreThreshold) {
                 output.asrAudioStart = inferenceWindow + preProcess.m_audioDataWindowSize;
-                output.asrAudioSamples = get_audio_array_size(currentIndex) -
-                                        (audioDataSlider.NextWindowStartIndex() -
-                                        preProcess.m_audioDataStride + preProcess.m_audioDataWindowSize);
+                output.asrAudioSamples =
+                    GetAudioArraySize(currentIndex) -
+                    (audioDataSlider.NextWindowStartIndex() - preProcess.m_audioDataStride +
+                     preProcess.m_audioDataWindowSize);
                 break;
             }
 
@@ -186,8 +192,8 @@
 
         /* 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);
 
         if (!PresentInferenceResult(finalResults)) {
             return output;
@@ -208,12 +214,12 @@
      **/
     static bool doAsr(ApplicationContext& ctx, const KWSOutput& kwsOutput)
     {
-        auto& asrModel = ctx.Get<Model&>("asrModel");
-        auto& profiler = ctx.Get<Profiler&>("profiler");
-        auto asrMfccFrameLen = ctx.Get<uint32_t>("asrFrameLength");
+        auto& asrModel          = ctx.Get<Model&>("asrModel");
+        auto& profiler          = ctx.Get<Profiler&>("profiler");
+        auto asrMfccFrameLen    = ctx.Get<uint32_t>("asrFrameLength");
         auto asrMfccFrameStride = ctx.Get<uint32_t>("asrFrameStride");
-        auto asrScoreThreshold = ctx.Get<float>("asrScoreThreshold");
-        auto asrInputCtxLen = ctx.Get<uint32_t>("ctxLen");
+        auto asrScoreThreshold  = ctx.Get<float>("asrScoreThreshold");
+        auto asrInputCtxLen     = ctx.Get<uint32_t>("ctxLen");
 
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
@@ -226,31 +232,32 @@
         hal_lcd_clear(COLOR_BLACK);
 
         /* Get Input and Output tensors for pre/post processing. */
-        TfLiteTensor* asrInputTensor = asrModel.GetInputTensor(0);
+        TfLiteTensor* asrInputTensor  = asrModel.GetInputTensor(0);
         TfLiteTensor* asrOutputTensor = asrModel.GetOutputTensor(0);
 
         /* Get input shape. Dimensions of the tensor should have been verified by
-        * the callee. */
+         * the callee. */
         TfLiteIntArray* inputShape = asrModel.GetInputShape(0);
 
-
         const uint32_t asrInputRows = asrInputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx];
         const uint32_t asrInputInnerLen = asrInputRows - (2 * asrInputCtxLen);
 
         /* Make sure the input tensor supports the above context and inner lengths. */
         if (asrInputRows <= 2 * asrInputCtxLen || asrInputRows <= asrInputInnerLen) {
             printf_err("ASR input rows not compatible with ctx length %" PRIu32 "\n",
-                asrInputCtxLen);
+                       asrInputCtxLen);
             return false;
         }
 
         /* Audio data stride corresponds to inputInnerLen feature vectors. */
-        const uint32_t asrAudioDataWindowLen = (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
+        const uint32_t asrAudioDataWindowLen =
+            (asrInputRows - 1) * asrMfccFrameStride + (asrMfccFrameLen);
         const uint32_t asrAudioDataWindowStride = asrInputInnerLen * asrMfccFrameStride;
-        const float asrAudioParamsSecondsPerSample = 1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
+        const float asrAudioParamsSecondsPerSample =
+            1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq;
 
         /* Get the remaining audio buffer and respective size from KWS results. */
-        const int16_t* audioArr = kwsOutput.asrAudioStart;
+        const int16_t* audioArr     = kwsOutput.asrAudioStart;
         const uint32_t audioArrSize = kwsOutput.asrAudioSamples;
 
         /* Audio clip must have enough samples to produce 1 MFCC feature. */
@@ -262,35 +269,40 @@
         }
 
         /* Initialise an audio slider. */
-        auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>(
-                audioBuffer.data(),
-                audioBuffer.size(),
-                asrAudioDataWindowLen,
-                asrAudioDataWindowStride);
+        auto audioDataSlider =
+            audio::FractionalSlidingWindow<const int16_t>(audioBuffer.data(),
+                                                          audioBuffer.size(),
+                                                          asrAudioDataWindowLen,
+                                                          asrAudioDataWindowStride);
 
         /* Declare a container for results. */
         std::vector<asr::AsrResult> asrResults;
 
         /* Display message on the LCD - inference running. */
         std::string str_inf{"Running ASR inference... "};
-        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);
 
         size_t asrInferenceWindowLen = asrAudioDataWindowLen;
 
         /* Set up pre and post-processing objects. */
-        AsrPreProcess asrPreProcess = AsrPreProcess(asrInputTensor, arm::app::Wav2LetterModel::ms_numMfccFeatures,
-                                              inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
-                                              asrMfccFrameLen, asrMfccFrameStride);
+        AsrPreProcess asrPreProcess =
+            AsrPreProcess(asrInputTensor,
+                          arm::app::Wav2LetterModel::ms_numMfccFeatures,
+                          inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
+                          asrMfccFrameLen,
+                          asrMfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
         const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(asrModel, asrInputCtxLen);
-        AsrPostProcess asrPostProcess = AsrPostProcess(
-                asrOutputTensor, ctx.Get<AsrClassifier&>("asrClassifier"),
-                ctx.Get<std::vector<std::string>&>("asrLabels"),
-                singleInfResult, outputCtxLen,
-                Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
-        );
+        AsrPostProcess asrPostProcess =
+            AsrPostProcess(asrOutputTensor,
+                           ctx.Get<AsrClassifier&>("asrClassifier"),
+                           ctx.Get<std::vector<std::string>&>("asrLabels"),
+                           singleInfResult,
+                           outputCtxLen,
+                           Wav2LetterModel::ms_blankTokenIdx,
+                           Wav2LetterModel::ms_outputRowsIdx);
         /* Start sliding through audio clip. */
         while (audioDataSlider.HasNext()) {
 
@@ -302,8 +314,9 @@
 
             const int16_t* asrInferenceWindow = audioDataSlider.Next();
 
-            info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
-                static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
+            info("Inference %zu/%zu\n",
+                 audioDataSlider.Index() + 1,
+                 static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
             /* Run the pre-processing, inference and post-processing. */
             if (!asrPreProcess.DoPreProcess(asrInferenceWindow, asrInferenceWindowLen)) {
@@ -327,25 +340,27 @@
             /* Get results. */
             std::vector<ClassificationResult> asrClassificationResult;
             auto& asrClassifier = ctx.Get<AsrClassifier&>("asrClassifier");
-            asrClassifier.GetClassificationResults(
-                    asrOutputTensor, asrClassificationResult,
-                    ctx.Get<std::vector<std::string>&>("asrLabels"), 1);
+            asrClassifier.GetClassificationResults(asrOutputTensor,
+                                                   asrClassificationResult,
+                                                   ctx.Get<std::vector<std::string>&>("asrLabels"),
+                                                   1);
 
-            asrResults.emplace_back(asr::AsrResult(asrClassificationResult,
-                                                (audioDataSlider.Index() *
-                                                 asrAudioParamsSecondsPerSample *
-                                                 asrAudioDataWindowStride),
-                                                 audioDataSlider.Index(), asrScoreThreshold));
+            asrResults.emplace_back(
+                asr::AsrResult(asrClassificationResult,
+                               (audioDataSlider.Index() * asrAudioParamsSecondsPerSample *
+                                asrAudioDataWindowStride),
+                               audioDataSlider.Index(),
+                               asrScoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-            armDumpTensor(asrOutputTensor, asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
+            armDumpTensor(asrOutputTensor,
+                          asrOutputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
 #endif /* VERIFY_TEST_OUTPUT */
 
             /* Erase */
             str_inf = std::string(str_inf.size(), ' ');
             hal_lcd_display_text(
-                        str_inf.c_str(), str_inf.size(),
-                        dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
+                str_inf.c_str(), str_inf.size(), dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
         }
         if (!PresentInferenceResult(asrResults)) {
             return false;
@@ -363,7 +378,7 @@
 
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!SetAppCtxIfmIdx(ctx, clipIndex,"kws_asr")) {
+            if (!SetAppCtxIfmIdx(ctx, clipIndex, "kws_asr")) {
                 return false;
             }
         }
@@ -379,13 +394,13 @@
 
             if (kwsOutput.asrAudioStart != nullptr && kwsOutput.asrAudioSamples > 0) {
                 info("Trigger keyword spotted\n");
-                if(!doAsr(ctx, kwsOutput)) {
+                if (!doAsr(ctx, kwsOutput)) {
                     printf_err("ASR failed\n");
                     return false;
                 }
             }
 
-            IncrementAppCtxIfmIdx(ctx,"kws_asr");
+            IncrementAppCtxIfmIdx(ctx, "kws_asr");
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
@@ -396,36 +411,38 @@
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
-        constexpr uint32_t dataPsnTxtYIncr   = 16;  /* Row index increment. */
+        constexpr uint32_t dataPsnTxtYIncr   = 16; /* Row index increment. */
 
         hal_lcd_set_text_color(COLOR_GREEN);
 
         /* Display each result. */
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
 
-        for (auto & result : results) {
+        for (auto& result : results) {
             std::string topKeyword{"<none>"};
             float score = 0.f;
 
             if (!result.m_resultVec.empty()) {
                 topKeyword = result.m_resultVec[0].m_label;
-                score = result.m_resultVec[0].m_normalisedVal;
+                score      = result.m_resultVec[0].m_normalisedVal;
             }
 
-            std::string resultStr =
-                    std::string{"@"} + std::to_string(result.m_timeStamp) +
-                    std::string{"s: "} + topKeyword + std::string{" ("} +
-                    std::to_string(static_cast<int>(score * 100)) + std::string{"%)"};
+            std::string resultStr = 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(),
-                    dataPsnTxtStartX1, rowIdx1, 0);
+            hal_lcd_display_text(
+                resultStr.c_str(), resultStr.size(), dataPsnTxtStartX1, rowIdx1, 0);
             rowIdx1 += dataPsnTxtYIncr;
 
             info("For timestamp: %f (inference #: %" PRIu32 "); threshold: %f\n",
-                 result.m_timeStamp, result.m_inferenceNumber,
+                 result.m_timeStamp,
+                 result.m_inferenceNumber,
                  result.m_threshold);
             for (uint32_t j = 0; j < result.m_resultVec.size(); ++j) {
-                info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n", j,
+                info("\t\tlabel @ %" PRIu32 ": %s, score: %f\n",
+                     j,
                      result.m_resultVec[j].m_label.c_str(),
                      result.m_resultVec[j].m_normalisedVal);
             }
@@ -438,30 +455,32 @@
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 80;
-        constexpr bool allow_multiple_lines = true;
+        constexpr bool allow_multiple_lines  = true;
 
         hal_lcd_set_text_color(COLOR_GREEN);
 
         /* Results from multiple inferences should be combined before processing. */
         std::vector<arm::app::ClassificationResult> combinedResults;
         for (auto& result : results) {
-            combinedResults.insert(combinedResults.end(),
-                                   result.m_resultVec.begin(),
-                                   result.m_resultVec.end());
+            combinedResults.insert(
+                combinedResults.end(), result.m_resultVec.begin(), result.m_resultVec.end());
         }
 
         for (auto& result : results) {
             /* Get the final result string using the decoder. */
             std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
 
-            info("Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber,
-                 infResultStr.c_str());
+            info(
+                "Result for inf %" PRIu32 ": %s\n", result.m_inferenceNumber, infResultStr.c_str());
         }
 
         std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
 
-        hal_lcd_display_text(finalResultStr.c_str(), finalResultStr.size(),
-                dataPsnTxtStartX1, dataPsnTxtStartY1, allow_multiple_lines);
+        hal_lcd_display_text(finalResultStr.c_str(),
+                             finalResultStr.size(),
+                             dataPsnTxtStartX1,
+                             dataPsnTxtStartY1,
+                             allow_multiple_lines);
 
         info("Final result: %s\n", finalResultStr.c_str());
         return true;