MLECO-3174: Minor refactoring to implemented use case APIS

Looks large but it is mainly just many small adjustments
Removed the inference runner code as it wasn't used
Fixes to doc strings
Consistent naming e.g. Asr/Kws instead of ASR/KWS

Signed-off-by: Richard Burton <richard.burton@arm.com>
Change-Id: I43b620b5c51d7910a29a63b509ac4d8a82c3a8fc

22 files changed, 343 insertions, 393 deletions

diff --git a/source/application/main/include/UseCaseCommonUtils.hpp b/source/application/main/include/UseCaseCommonUtils.hpp
index f79f6ed..9b6d550 100644
--- a/source/application/main/include/UseCaseCommonUtils.hpp
+++ b/source/application/main/include/UseCaseCommonUtils.hpp
@@ -24,7 +24,6 @@
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
 #include "Classifier.hpp"           /* Classifier. */
 #include "InputFiles.hpp"
-#include "BaseProcessing.hpp"
 
 
 void DisplayCommonMenu();
@@ -108,67 +107,6 @@ namespace app {
      **/
     bool ListFilesHandler(ApplicationContext& ctx);
 
-    /**
-     * @brief   Use case runner class that will handle calling pre-processing,
-     *          inference and post-processing.
-     *          After constructing an instance of this class the user can call
-     *          PreProcess(), RunInference() and PostProcess() to perform inference.
-     */
-    class UseCaseRunner {
-
-    private:
-        BasePreProcess* m_preProcess;
-        BasePostProcess* m_postProcess;
-        Model* m_model;
-
-    public:
-        explicit UseCaseRunner(BasePreProcess* preprocess, BasePostProcess* postprocess, Model* model)
-        : m_preProcess{preprocess},
-          m_postProcess{postprocess},
-          m_model{model}
-          {};
-
-        /**
-         * @brief       Runs pre-processing as defined by PreProcess object within the runner.
-         *              Templated for the input data type.
-         * @param[in]   inputData    Pointer to the data that inference will be performed on.
-         * @param[in]   inputSize    Size of the input data that inference will be performed on.
-         * @return      true if successful, false otherwise.
-         **/
-        template<typename T>
-        bool PreProcess(T* inputData, size_t inputSize) {
-            if (!this->m_preProcess->DoPreProcess(inputData, inputSize)) {
-                printf_err("Pre-processing failed.");
-                return false;
-            }
-            return true;
-        }
-
-        /**
-         * @brief       Runs inference with the Model object within the runner.
-         * @return      true if successful, false otherwise.
-         **/
-        bool RunInference() {
-            if (!this->m_model->RunInference()) {
-                printf_err("Inference failed.");
-                return false;
-            }
-            return true;
-        }
-
-        /**
-         * @brief       Runs post-processing as defined by PostProcess object within the runner.
-         * @return      true if successful, false otherwise.
-         **/
-        bool PostProcess() {
-            if (!this->m_postProcess->DoPostProcess()) {
-                printf_err("Post-processing failed.");
-                return false;
-            }
-            return true;
-        }
-    };
-
 } /* namespace app */
 } /* namespace arm */
 
diff --git a/source/use_case/asr/include/AsrClassifier.hpp b/source/use_case/asr/include/AsrClassifier.hpp
index 67a200e..a07a721 100644
--- a/source/use_case/asr/include/AsrClassifier.hpp
+++ b/source/use_case/asr/include/AsrClassifier.hpp
@@ -35,10 +35,10 @@ namespace app {
          * @param[in]   use_softmax    Whether softmax scaling should be applied to model output.
          * @return      true if successful, false otherwise.
          **/
-        bool GetClassificationResults(
-            TfLiteTensor* outputTensor,
-            std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax = false) override;
+        bool GetClassificationResults(TfLiteTensor* outputTensor,
+                                      std::vector<ClassificationResult>& vecResults,
+                                      const std::vector<std::string>& labels,
+                                      uint32_t topNCount, bool use_softmax = false) override;
 
     private:
         /**
@@ -54,7 +54,7 @@ namespace app {
         template<typename T>
         bool GetTopResults(TfLiteTensor* tensor,
                            std::vector<ClassificationResult>& vecResults,
-                           const std::vector <std::string>& labels, double scale, double zeroPoint);
+                           const std::vector<std::string>& labels, double scale, double zeroPoint);
     };
 
 } /* namespace app */
diff --git a/source/use_case/asr/include/Wav2LetterModel.hpp b/source/use_case/asr/include/Wav2LetterModel.hpp
index 895df2b..0078e44 100644
--- a/source/use_case/asr/include/Wav2LetterModel.hpp
+++ b/source/use_case/asr/include/Wav2LetterModel.hpp
@@ -36,6 +36,7 @@ namespace app {
         static constexpr uint32_t ms_outputRowsIdx = 2;
         static constexpr uint32_t ms_outputColsIdx = 3;
 
+        /* Model specific constants. */
         static constexpr uint32_t ms_blankTokenIdx   = 28;
         static constexpr uint32_t ms_numMfccFeatures = 13;
 
diff --git a/source/use_case/asr/include/Wav2LetterPostprocess.hpp b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
index 45defa5..446014d 100644
--- a/source/use_case/asr/include/Wav2LetterPostprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPostprocess.hpp
@@ -30,23 +30,24 @@ namespace app {
      * @brief   Helper class to manage tensor post-processing for "wav2letter"
      *          output.
      */
-    class ASRPostProcess : public BasePostProcess {
+    class AsrPostProcess : public BasePostProcess {
     public:
         bool m_lastIteration = false;   /* Flag to set if processing the last set of data for a clip. */
 
         /**
          * @brief           Constructor
-         * @param[in]       outputTensor       Pointer to the output Tensor.
+         * @param[in]       outputTensor       Pointer to the TFLite Micro output Tensor.
+         * @param[in]       classifier         Object used to get top N results from classification.
          * @param[in]       labels             Vector of string labels to identify each output of the model.
-         * @param[in/out]   result            Vector of classification results to store decoded outputs.
+         * @param[in/out]   result             Vector of classification results to store decoded outputs.
          * @param[in]       outputContextLen   Left/right context length for output tensor.
          * @param[in]       blankTokenIdx      Index in the labels that the "Blank token" takes.
          * @param[in]       reductionAxis      The axis that the logits of each time step is on.
          **/
-        ASRPostProcess(AsrClassifier& classifier, TfLiteTensor* outputTensor,
-                const std::vector<std::string>& labels, asr::ResultVec& result,
-                uint32_t outputContextLen,
-                uint32_t blankTokenIdx, uint32_t reductionAxis);
+        AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier,
+                       const std::vector<std::string>& labels, asr::ResultVec& result,
+                       uint32_t outputContextLen,
+                       uint32_t blankTokenIdx, uint32_t reductionAxis);
 
         /**
          * @brief    Should perform post-processing of the result of inference then
diff --git a/source/use_case/asr/include/Wav2LetterPreprocess.hpp b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
index 8c12b3d..dc9a415 100644
--- a/source/use_case/asr/include/Wav2LetterPreprocess.hpp
+++ b/source/use_case/asr/include/Wav2LetterPreprocess.hpp
@@ -31,22 +31,22 @@ namespace app {
      * for ASR. */
     using AudioWindow = audio::SlidingWindow<const int16_t>;
 
-    class ASRPreProcess : public BasePreProcess {
+    class AsrPreProcess : public BasePreProcess {
     public:
         /**
          * @brief       Constructor.
          * @param[in]   inputTensor        Pointer to the TFLite Micro input Tensor.
          * @param[in]   numMfccFeatures    Number of MFCC features per window.
+         * @param[in]   numFeatureFrames   Number of MFCC vectors that need to be calculated
+         *                                 for an inference.
          * @param[in]   mfccWindowLen      Number of audio elements to calculate MFCC features per window.
          * @param[in]   mfccWindowStride   Stride (in number of elements) for moving the MFCC window.
-         * @param[in]   mfccWindowStride   Number of MFCC vectors that need to be calculated
-         *                                 for an inference.
          */
-        ASRPreProcess(TfLiteTensor* inputTensor,
-                uint32_t  numMfccFeatures,
-                uint32_t  audioWindowLen,
-                uint32_t  mfccWindowLen,
-                uint32_t  mfccWindowStride);
+        AsrPreProcess(TfLiteTensor* inputTensor,
+                      uint32_t  numMfccFeatures,
+                      uint32_t  numFeatureFrames,
+                      uint32_t  mfccWindowLen,
+                      uint32_t  mfccWindowStride);
 
         /**
          * @brief       Calculates the features required from audio data. This
@@ -130,9 +130,9 @@ namespace app {
             }
 
             /* Populate. */
-            T * outputBufMfcc = outputBuf;
-            T * outputBufD1 = outputBuf + this->m_numMfccFeats;
-            T * outputBufD2 = outputBufD1 + this->m_numMfccFeats;
+            T* outputBufMfcc = outputBuf;
+            T* outputBufD1 = outputBuf + this->m_numMfccFeats;
+            T* outputBufD2 = outputBufD1 + this->m_numMfccFeats;
             const uint32_t ptrIncr = this->m_numMfccFeats * 2;  /* (3 vectors - 1 vector) */
 
             const float minVal = std::numeric_limits<T>::min();
@@ -141,13 +141,13 @@ namespace app {
             /* Need to transpose while copying and concatenating the tensor. */
             for (uint32_t j = 0; j < this->m_numFeatureFrames; ++j) {
                 for (uint32_t i = 0; i < this->m_numMfccFeats; ++i) {
-                    *outputBufMfcc++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+                    *outputBufMfcc++ = static_cast<T>(AsrPreProcess::GetQuantElem(
                             this->m_mfccBuf(i, j), quantScale,
                             quantOffset, minVal, maxVal));
-                    *outputBufD1++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+                    *outputBufD1++ = static_cast<T>(AsrPreProcess::GetQuantElem(
                             this->m_delta1Buf(i, j), quantScale,
                             quantOffset, minVal, maxVal));
-                    *outputBufD2++ = static_cast<T>(ASRPreProcess::GetQuantElem(
+                    *outputBufD2++ = static_cast<T>(AsrPreProcess::GetQuantElem(
                             this->m_delta2Buf(i, j), quantScale,
                             quantOffset, minVal, maxVal));
                 }
diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc
index 84e66b7..4ba8c7b 100644
--- a/source/use_case/asr/src/AsrClassifier.cc
+++ b/source/use_case/asr/src/AsrClassifier.cc
@@ -20,117 +20,125 @@
 #include "TensorFlowLiteMicro.hpp"
 #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)
-{
-    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;
-    }
+namespace arm {
+namespace app {
+
+    template<typename T>
+    bool 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[Wav2LetterModel::ms_outputRowsIdx];
+        const uint32_t nLetters = tensor->dims->data[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) {
-        return false;
-    }
+        /* NOTE: tensor's size verification against labels should be
+         *       checked by the calling/public function. */
+        if (nLetters < 1) {
+            return false;
+        }
 
-    /* Final results' container. */
-    vecResults = std::vector<ClassificationResult>(nElems);
+        /* Final results' container. */
+        vecResults = std::vector<ClassificationResult>(nElems);
 
-    T* tensorData = tflite::GetTensorData<T>(tensor);
+        T* tensorData = tflite::GetTensorData<T>(tensor);
 
-    /* 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);
+        /* 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);
 
-        for (uint32_t j = 1; j < nLetters; ++j) {
-            if (top_1.first < tensorData[row + j]) {
-                top_1.first = tensorData[row + j];
-                top_1.second = j;
+            for (uint32_t j = 1; j < nLetters; ++j) {
+                if (top_1.first < tensorData[row + j]) {
+                    top_1.first = tensorData[row + j];
+                    top_1.second = j;
+                }
             }
+
+            double score = static_cast<int> (top_1.first);
+            vecResults[i].m_normalisedVal = scale * (score - zeroPoint);
+            vecResults[i].m_label = labels[top_1.second];
+            vecResults[i].m_labelIdx = top_1.second;
         }
 
-        double score = static_cast<int> (top_1.first);
-        vecResults[i].m_normalisedVal = scale * (score - zeroPoint);
-        vecResults[i].m_label = labels[top_1.second];
-        vecResults[i].m_labelIdx = top_1.second;
+        return true;
     }
-
-    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);
-
-bool arm::app::AsrClassifier::GetClassificationResults(
+    template bool AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+                                                        std::vector<ClassificationResult>& vecResults,
+                                                        const std::vector <std::string>& labels,
+                                                        double scale, double zeroPoint);
+    template bool AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+                                                       std::vector<ClassificationResult>& vecResults,
+                                                       const std::vector <std::string>& labels,
+                                                       double scale, double zeroPoint);
+
+    bool AsrClassifier::GetClassificationResults(
             TfLiteTensor* outputTensor,
             std::vector<ClassificationResult>& vecResults,
             const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
-{
-        UNUSED(use_softmax);
-        vecResults.clear();
+    {
+            UNUSED(use_softmax);
+            vecResults.clear();
 
-        constexpr int minTensorDims = static_cast<int>(
-            (arm::app::Wav2LetterModel::ms_outputRowsIdx > arm::app::Wav2LetterModel::ms_outputColsIdx)?
-             arm::app::Wav2LetterModel::ms_outputRowsIdx : arm::app::Wav2LetterModel::ms_outputColsIdx);
+            constexpr int minTensorDims = static_cast<int>(
+                (Wav2LetterModel::ms_outputRowsIdx > Wav2LetterModel::ms_outputColsIdx)?
+                 Wav2LetterModel::ms_outputRowsIdx : Wav2LetterModel::ms_outputColsIdx);
 
-        constexpr uint32_t outColsIdx = arm::app::Wav2LetterModel::ms_outputColsIdx;
+            constexpr uint32_t outColsIdx = Wav2LetterModel::ms_outputColsIdx;
 
-        /* Sanity checks. */
-        if (outputTensor == nullptr) {
-            printf_err("Output vector is null pointer.\n");
-            return false;
-        } else if (outputTensor->dims->size < minTensorDims) {
-            printf_err("Output tensor expected to be %dD\n", minTensorDims);
-            return false;
-        } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) {
-            printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount);
-            return false;
-        } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) {
-            printf("Output size doesn't match the labels' size\n");
-            return false;
-        }
+            /* Sanity checks. */
+            if (outputTensor == nullptr) {
+                printf_err("Output vector is null pointer.\n");
+                return false;
+            } else if (outputTensor->dims->size < minTensorDims) {
+                printf_err("Output tensor expected to be %dD\n", minTensorDims);
+                return false;
+            } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) {
+                printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount);
+                return false;
+            } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) {
+                printf("Output size doesn't match the labels' size\n");
+                return false;
+            }
 
-        if (topNCount != 1) {
-            warn("TopNCount value ignored in this implementation\n");
-        }
+            if (topNCount != 1) {
+                warn("TopNCount value ignored in this implementation\n");
+            }
 
-        /* To return the floating point values, we need quantization parameters. */
-        QuantParams quantParams = GetTensorQuantParams(outputTensor);
-
-        bool resultState;
-
-        switch (outputTensor->type) {
-            case kTfLiteUInt8:
-                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);
-                break;
-            default:
-                printf_err("Tensor type %s not supported by classifier\n",
-                    TfLiteTypeGetName(outputTensor->type));
+            /* To return the floating point values, we need quantization parameters. */
+            QuantParams quantParams = GetTensorQuantParams(outputTensor);
+
+            bool resultState;
+
+            switch (outputTensor->type) {
+                case kTfLiteUInt8:
+                    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);
+                    break;
+                default:
+                    printf_err("Tensor type %s not supported by classifier\n",
+                        TfLiteTypeGetName(outputTensor->type));
+                    return false;
+            }
+
+            if (!resultState) {
+                printf_err("Failed to get sorted set\n");
                 return false;
-        }
+            }
 
-        if (!resultState) {
-            printf_err("Failed to get sorted set\n");
-            return false;
-        }
+            return true;
+    }
 
-        return true;
-}
\ No newline at end of file
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 7fe959b..850bdc2 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -33,9 +33,9 @@ namespace arm {
 namespace app {
 
     /**
-     * @brief           Presents ASR inference results.
-     * @param[in]       results     Vector of ASR classification results to be displayed.
-     * @return          true if successful, false otherwise.
+     * @brief       Presents ASR inference results.
+     * @param[in]   results   Vector of ASR classification results to be displayed.
+     * @return      true if successful, false otherwise.
      **/
     static bool PresentInferenceResult(const std::vector<asr::AsrResult>& results);
 
@@ -63,6 +63,9 @@ namespace app {
             return false;
         }
 
+        TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
         /* Get input shape. Dimensions of the tensor should have been verified by
          * the callee. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
@@ -78,19 +81,19 @@ namespace app {
         const float secondsPerSample = (1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
 
         /* Set up pre and post-processing objects. */
-        ASRPreProcess preProcess = ASRPreProcess(model.GetInputTensor(0), Wav2LetterModel::ms_numMfccFeatures,
-                inputShape->data[Wav2LetterModel::ms_inputRowsIdx], mfccFrameLen, mfccFrameStride);
+        AsrPreProcess preProcess = AsrPreProcess(inputTensor, Wav2LetterModel::ms_numMfccFeatures,
+                                                 inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
+                                                 mfccFrameLen, mfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
-        const uint32_t outputCtxLen = ASRPostProcess::GetOutputContextLen(model, inputCtxLen);
-        ASRPostProcess postProcess = ASRPostProcess(ctx.Get<AsrClassifier&>("classifier"),
-                model.GetOutputTensor(0), ctx.Get<std::vector<std::string>&>("labels"),
+        const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(model, inputCtxLen);
+        AsrPostProcess postProcess = AsrPostProcess(
+                outputTensor, ctx.Get<AsrClassifier&>("classifier"),
+                ctx.Get<std::vector<std::string>&>("labels"),
                 singleInfResult, outputCtxLen,
                 Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
                 );
 
-        UseCaseRunner runner = UseCaseRunner(&preProcess, &postProcess, &model);
-
         /* Loop to process audio clips. */
         do {
             hal_lcd_clear(COLOR_BLACK);
@@ -147,16 +150,20 @@ namespace app {
                      static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
                 /* Run the pre-processing, inference and post-processing. */
-                runner.PreProcess(inferenceWindow, inferenceWindowLen);
+                if (!preProcess.DoPreProcess(inferenceWindow, inferenceWindowLen)) {
+                    printf_err("Pre-processing failed.");
+                    return false;
+                }
 
-                profiler.StartProfiling("Inference");
-                if (!runner.RunInference()) {
+                if (!RunInference(model, profiler)) {
+                    printf_err("Inference failed.");
                     return false;
                 }
-                profiler.StopProfiling();
 
+                /* Post processing needs to know if we are on the last audio window. */
                 postProcess.m_lastIteration = !audioDataSlider.HasNext();
-                if (!runner.PostProcess()) {
+                if (!postProcess.DoPostProcess()) {
+                    printf_err("Post-processing failed.");
                     return false;
                 }
 
@@ -166,7 +173,6 @@ namespace app {
                         audioDataSlider.Index(), scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
                 armDumpTensor(outputTensor,
                     outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
 #endif /* VERIFY_TEST_OUTPUT */
diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc
index e3e1999..42f434e 100644
--- a/source/use_case/asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc
@@ -24,7 +24,7 @@
 namespace arm {
 namespace app {
 
-    ASRPostProcess::ASRPostProcess(AsrClassifier& classifier, TfLiteTensor* outputTensor,
+    AsrPostProcess::AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier,
             const std::vector<std::string>& labels, std::vector<ClassificationResult>& results,
             const uint32_t outputContextLen,
             const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx
@@ -38,11 +38,11 @@ namespace app {
             m_blankTokenIdx(blankTokenIdx),
             m_reductionAxisIdx(reductionAxisIdx)
     {
-        this->m_outputInnerLen = ASRPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
+        this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
         this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen);
     }
 
-    bool ASRPostProcess::DoPostProcess()
+    bool AsrPostProcess::DoPostProcess()
     {
         /* Basic checks. */
         if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) {
@@ -51,7 +51,7 @@ namespace app {
 
         /* Irrespective of tensor type, we use unsigned "byte" */
         auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor);
-        const uint32_t elemSz = ASRPostProcess::GetTensorElementSize(this->m_outputTensor);
+        const uint32_t elemSz = AsrPostProcess::GetTensorElementSize(this->m_outputTensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
@@ -79,7 +79,7 @@ namespace app {
         return true;
     }
 
-    bool ASRPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
+    bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -101,7 +101,7 @@ namespace app {
         return true;
     }
 
-    uint32_t ASRPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
+    uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
@@ -120,7 +120,7 @@ namespace app {
         return 0;
     }
 
-    bool ASRPostProcess::EraseSectionsRowWise(
+    bool AsrPostProcess::EraseSectionsRowWise(
             uint8_t*         ptrData,
             const uint32_t   strideSzBytes,
             const bool       lastIteration)
@@ -157,7 +157,7 @@ namespace app {
         return true;
     }
 
-    uint32_t ASRPostProcess::GetNumFeatureVectors(const Model& model)
+    uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model)
     {
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
         const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0);
@@ -168,21 +168,23 @@ namespace app {
         return inputRows;
     }
 
-    uint32_t ASRPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen)
+    uint32_t AsrPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen)
     {
         const uint32_t outputRows = std::max(outputTensor->dims->data[Wav2LetterModel::ms_outputRowsIdx], 0);
         if (outputRows == 0) {
             printf_err("Error getting number of output rows for axis: %" PRIu32 "\n",
                     Wav2LetterModel::ms_outputRowsIdx);
         }
+
+        /* Watching for underflow. */
         int innerLen = (outputRows - (2 * outputCtxLen));
 
         return std::max(innerLen, 0);
     }
 
-    uint32_t ASRPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen)
+    uint32_t AsrPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen)
     {
-        const uint32_t inputRows = ASRPostProcess::GetNumFeatureVectors(model);
+        const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model);
         const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
         constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx;
 
diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc
index 590d08a..92b0631 100644
--- a/source/use_case/asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc
@@ -25,9 +25,9 @@
 namespace arm {
 namespace app {
 
-    ASRPreProcess::ASRPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures,
-            const uint32_t numFeatureFrames, const uint32_t mfccWindowLen,
-            const uint32_t mfccWindowStride
+    AsrPreProcess::AsrPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures,
+                                 const uint32_t numFeatureFrames, const uint32_t mfccWindowLen,
+                                 const uint32_t mfccWindowStride
             ):
             m_mfcc(numMfccFeatures, mfccWindowLen),
             m_inputTensor(inputTensor),
@@ -44,7 +44,7 @@ namespace app {
         }
     }
 
-    bool ASRPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
+    bool AsrPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
     {
         this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(
                 static_cast<const int16_t*>(audioData), audioDataLen,
@@ -82,7 +82,7 @@ namespace app {
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        ASRPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
+        AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
 
         /* Standardize calculated features. */
         this->Standarize();
@@ -112,9 +112,9 @@ namespace app {
         return false;
     }
 
-    bool ASRPreProcess::ComputeDeltas(Array2d<float>& mfcc,
-                                   Array2d<float>& delta1,
-                                   Array2d<float>& delta2)
+    bool AsrPreProcess::ComputeDeltas(Array2d<float>& mfcc,
+                                      Array2d<float>& delta1,
+                                      Array2d<float>& delta2)
     {
         const std::vector <float> delta1Coeffs =
             {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
@@ -167,7 +167,7 @@ namespace app {
         return true;
     }
 
-    void ASRPreProcess::StandardizeVecF32(Array2d<float>& vec)
+    void AsrPreProcess::StandardizeVecF32(Array2d<float>& vec)
     {
         auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
         auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
@@ -186,14 +186,14 @@ namespace app {
         }
     }
 
-    void ASRPreProcess::Standarize()
+    void AsrPreProcess::Standarize()
     {
-        ASRPreProcess::StandardizeVecF32(this->m_mfccBuf);
-        ASRPreProcess::StandardizeVecF32(this->m_delta1Buf);
-        ASRPreProcess::StandardizeVecF32(this->m_delta2Buf);
+        AsrPreProcess::StandardizeVecF32(this->m_mfccBuf);
+        AsrPreProcess::StandardizeVecF32(this->m_delta1Buf);
+        AsrPreProcess::StandardizeVecF32(this->m_delta2Buf);
     }
 
-    float ASRPreProcess::GetQuantElem(
+    float AsrPreProcess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,
diff --git a/source/use_case/img_class/include/ImgClassProcessing.hpp b/source/use_case/img_class/include/ImgClassProcessing.hpp
index 59db4a5..e931b7d 100644
--- a/source/use_case/img_class/include/ImgClassProcessing.hpp
+++ b/source/use_case/img_class/include/ImgClassProcessing.hpp
@@ -34,9 +34,10 @@ namespace app {
     public:
         /**
          * @brief       Constructor
-         * @param[in]   model   Pointer to the the Image classification Model object.
+         * @param[in]   inputTensor     Pointer to the TFLite Micro input Tensor.
+         * @param[in]   convertToInt8   Should the image be converted to Int8 range.
          **/
-        explicit ImgClassPreProcess(Model* model);
+        explicit ImgClassPreProcess(TfLiteTensor* inputTensor, bool convertToInt8);
 
         /**
          * @brief       Should perform pre-processing of 'raw' input image data and load it into
@@ -46,6 +47,10 @@ namespace app {
          * @return      true if successful, false otherwise.
          **/
         bool DoPreProcess(const void* input, size_t inputSize) override;
+
+    private:
+        TfLiteTensor* m_inputTensor;
+        bool m_convertToInt8;
     };
 
     /**
@@ -55,29 +60,30 @@ namespace app {
      */
     class ImgClassPostProcess : public BasePostProcess {
 
-    private:
-        Classifier& m_imgClassifier;
-        const std::vector<std::string>& m_labels;
-        std::vector<ClassificationResult>& m_results;
-
     public:
         /**
          * @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]   outputTensor  Pointer to the TFLite Micro output Tensor.
+         * @param[in]   classifier    Classifier object used to get top N results from classification.
+         * @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.
          **/
-        ImgClassPostProcess(Classifier& classifier, Model* model,
+        ImgClassPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
                             const std::vector<std::string>& labels,
                             std::vector<ClassificationResult>& results);
 
         /**
-         * @brief       Should perform post-processing of the result of inference then populate
+         * @brief       Should perform post-processing of the result of inference then
          *              populate classification result data for any later use.
          * @return      true if successful, false otherwise.
          **/
         bool DoPostProcess() override;
+
+    private:
+        TfLiteTensor* m_outputTensor;
+        Classifier& m_imgClassifier;
+        const std::vector<std::string>& m_labels;
+        std::vector<ClassificationResult>& m_results;
     };
 
 } /* namespace app */
diff --git a/source/use_case/img_class/src/ImgClassProcessing.cc b/source/use_case/img_class/src/ImgClassProcessing.cc
index 6ba88ad..adf9794 100644
--- a/source/use_case/img_class/src/ImgClassProcessing.cc
+++ b/source/use_case/img_class/src/ImgClassProcessing.cc
@@ -21,50 +21,43 @@
 namespace arm {
 namespace app {
 
-    ImgClassPreProcess::ImgClassPreProcess(Model* model)
-    {
-        if (!model->IsInited()) {
-            printf_err("Model is not initialised!.\n");
-        }
-        this->m_model = model;
-    }
+    ImgClassPreProcess::ImgClassPreProcess(TfLiteTensor* inputTensor, bool convertToInt8)
+    :m_inputTensor{inputTensor},
+     m_convertToInt8{convertToInt8}
+    {}
 
     bool ImgClassPreProcess::DoPreProcess(const void* data, size_t inputSize)
     {
         if (data == nullptr) {
             printf_err("Data pointer is null");
+            return false;
         }
 
         auto input = static_cast<const uint8_t*>(data);
-        TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
 
-        std::memcpy(inputTensor->data.data, input, inputSize);
+        std::memcpy(this->m_inputTensor->data.data, input, inputSize);
         debug("Input tensor populated \n");
 
-        if (this->m_model->IsDataSigned()) {
-            image::ConvertImgToInt8(inputTensor->data.data, inputTensor->bytes);
+        if (this->m_convertToInt8) {
+            image::ConvertImgToInt8(this->m_inputTensor->data.data, this->m_inputTensor->bytes);
         }
 
         return true;
     }
 
-    ImgClassPostProcess::ImgClassPostProcess(Classifier& classifier, Model* model,
+    ImgClassPostProcess::ImgClassPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
                                              const std::vector<std::string>& labels,
                                              std::vector<ClassificationResult>& results)
-            :m_imgClassifier{classifier},
+            :m_outputTensor{outputTensor},
+             m_imgClassifier{classifier},
              m_labels{labels},
              m_results{results}
-    {
-        if (!model->IsInited()) {
-            printf_err("Model is not initialised!.\n");
-        }
-        this->m_model = model;
-    }
+    {}
 
     bool ImgClassPostProcess::DoPostProcess()
     {
         return this->m_imgClassifier.GetClassificationResults(
-                this->m_model->GetOutputTensor(0), this->m_results,
+                this->m_outputTensor, this->m_results,
                 this->m_labels, 5, false);
     }
 
diff --git a/source/use_case/img_class/src/UseCaseHandler.cc b/source/use_case/img_class/src/UseCaseHandler.cc
index c68d816..5cc3959 100644
--- a/source/use_case/img_class/src/UseCaseHandler.cc
+++ b/source/use_case/img_class/src/UseCaseHandler.cc
@@ -59,6 +59,7 @@ namespace app {
         }
 
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -74,13 +75,12 @@ namespace app {
         const uint32_t nChannels = inputShape->data[arm::app::MobileNetModel::ms_inputChannelsIdx];
 
         /* Set up pre and post-processing. */
-        ImgClassPreProcess preprocess = ImgClassPreProcess(&model);
+        ImgClassPreProcess preProcess = ImgClassPreProcess(inputTensor, model.IsDataSigned());
 
         std::vector<ClassificationResult> results;
-        ImgClassPostProcess postprocess = ImgClassPostProcess(ctx.Get<ImgClassClassifier&>("classifier"), &model,
-                ctx.Get<std::vector<std::string>&>("labels"), results);
-
-        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
+        ImgClassPostProcess postProcess = ImgClassPostProcess(outputTensor,
+                ctx.Get<ImgClassClassifier&>("classifier"), ctx.Get<std::vector<std::string>&>("labels"),
+                results);
 
         do {
             hal_lcd_clear(COLOR_BLACK);
@@ -113,17 +113,18 @@ namespace app {
                                   inputTensor->bytes : IMAGE_DATA_SIZE;
 
             /* Run the pre-processing, inference and post-processing. */
-            if (!runner.PreProcess(imgSrc, imgSz)) {
+            if (!preProcess.DoPreProcess(imgSrc, imgSz)) {
+                printf_err("Pre-processing failed.");
                 return false;
             }
 
-            profiler.StartProfiling("Inference");
-            if (!runner.RunInference()) {
+            if (!RunInference(model, profiler)) {
+                printf_err("Inference failed.");
                 return false;
             }
-            profiler.StopProfiling();
 
-            if (!runner.PostProcess()) {
+            if (!postProcess.DoPostProcess()) {
+                printf_err("Post-processing failed.");
                 return false;
             }
 
@@ -136,7 +137,6 @@ namespace app {
             ctx.Set<std::vector<ClassificationResult>>("results", results);
 
 #if VERIFY_TEST_OUTPUT
-            TfLiteTensor* outputTensor = model.GetOutputTensor(0);
             arm::app::DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
 
diff --git a/source/use_case/kws/include/KwsProcessing.hpp b/source/use_case/kws/include/KwsProcessing.hpp
index ddf38c1..d3de3b3 100644
--- a/source/use_case/kws/include/KwsProcessing.hpp
+++ b/source/use_case/kws/include/KwsProcessing.hpp
@@ -33,18 +33,21 @@ namespace app {
      *          Implements methods declared by BasePreProcess and anything else needed
      *          to populate input tensors ready for inference.
      */
-    class KWSPreProcess : public BasePreProcess {
+    class KwsPreProcess : public BasePreProcess {
 
     public:
         /**
          * @brief       Constructor
-         * @param[in]   model             Pointer to 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.
+         * @param[in]   inputTensor        Pointer to the TFLite Micro input Tensor.
+         * @param[in]   numFeatures        How many MFCC features to use.
+         * @param[in]   numFeatureFrames   Number of MFCC vectors that need to be calculated
+         *                                 for an inference.
+         * @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);
+        explicit KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numFeatureFrames,
+                               int mfccFrameLength, int mfccFrameStride);
 
         /**
          * @brief       Should perform pre-processing of 'raw' input audio data and load it into
@@ -60,8 +63,10 @@ namespace app {
         size_t m_audioDataStride;       /* Amount of audio to stride across if doing >1 inference in longer clips. */
 
     private:
+        TfLiteTensor* m_inputTensor;    /* Model input tensor. */
         const int m_mfccFrameLength;
         const int m_mfccFrameStride;
+        const size_t m_numMfccFrames;   /* How many sets of m_numMfccFeats. */
 
         audio::MicroNetKwsMFCC m_mfcc;
         audio::SlidingWindow<const int16_t> m_mfccSlidingWindow;
@@ -99,22 +104,23 @@ namespace app {
      *          Implements methods declared by BasePostProcess and anything else needed
      *          to populate result vector.
      */
-    class KWSPostProcess : public BasePostProcess {
+    class KwsPostProcess : public BasePostProcess {
 
     private:
-        Classifier& m_kwsClassifier;
-        const std::vector<std::string>& m_labels;
-        std::vector<ClassificationResult>& m_results;
+        TfLiteTensor* m_outputTensor;                   /* Model output tensor. */
+        Classifier& m_kwsClassifier;                    /* KWS Classifier object. */
+        const std::vector<std::string>& m_labels;       /* KWS Labels. */
+        std::vector<ClassificationResult>& m_results;   /* Results vector for a single inference. */
 
     public:
         /**
          * @brief           Constructor
-         * @param[in]       classifier   Classifier object used to get top N results from classification.
-         * @param[in]       model        Pointer to the KWS Model object.
-         * @param[in]       labels       Vector of string labels to identify each output of the model.
-         * @param[in/out]   results      Vector of classification results to store decoded outputs.
+         * @param[in]       outputTensor   Pointer to the TFLite Micro output Tensor.
+         * @param[in]       classifier     Classifier object used to get top N results from classification.
+         * @param[in]       labels         Vector of string labels to identify each output of the model.
+         * @param[in/out]   results        Vector of classification results to store decoded outputs.
          **/
-        KWSPostProcess(Classifier& classifier, Model* model,
+        KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
                        const std::vector<std::string>& labels,
                        std::vector<ClassificationResult>& results);
 
diff --git a/source/use_case/kws/include/KwsResult.hpp b/source/use_case/kws/include/KwsResult.hpp
index 5a26ce1..38f32b4 100644
--- a/source/use_case/kws/include/KwsResult.hpp
+++ b/source/use_case/kws/include/KwsResult.hpp
@@ -25,7 +25,7 @@ namespace arm {
 namespace app {
 namespace kws {
 
-    using ResultVec = std::vector < arm::app::ClassificationResult >;
+    using ResultVec = std::vector<arm::app::ClassificationResult>;
 
     /* Structure for holding kws result. */
     class KwsResult {
diff --git a/source/use_case/kws/src/KwsProcessing.cc b/source/use_case/kws/src/KwsProcessing.cc
index 14f9fce..328709d 100644
--- a/source/use_case/kws/src/KwsProcessing.cc
+++ b/source/use_case/kws/src/KwsProcessing.cc
@@ -22,22 +22,19 @@
 namespace arm {
 namespace app {
 
-    KWSPreProcess::KWSPreProcess(Model* model, size_t numFeatures, int mfccFrameLength, int mfccFrameStride):
+    KwsPreProcess::KwsPreProcess(TfLiteTensor* inputTensor, size_t numFeatures, size_t numMfccFrames,
+            int mfccFrameLength, int mfccFrameStride
+        ):
+        m_inputTensor{inputTensor},
         m_mfccFrameLength{mfccFrameLength},
         m_mfccFrameStride{mfccFrameStride},
+        m_numMfccFrames{numMfccFrames},
         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_audioDataWindowSize = this->m_numMfccFrames * this->m_mfccFrameStride +
                 (this->m_mfccFrameLength - this->m_mfccFrameStride);
 
         /* Creating an MFCC feature sliding window for the data required for 1 inference. */
@@ -62,7 +59,7 @@ namespace app {
                 - this->m_numMfccVectorsInAudioStride;
 
         /* Construct feature calculation function. */
-        this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_model->GetInputTensor(0),
+        this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor,
                                                              this->m_numReusedMfccVectors);
 
         if (!this->m_mfccFeatureCalculator) {
@@ -70,7 +67,7 @@ namespace app {
         }
     }
 
-    bool KWSPreProcess::DoPreProcess(const void* data, size_t inputSize)
+    bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize)
     {
         UNUSED(inputSize);
         if (data == nullptr) {
@@ -116,8 +113,8 @@ namespace app {
      */
     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)
+    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);
@@ -149,18 +146,18 @@ namespace app {
     }
 
     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);
+    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);
+    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)
+    KwsPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
     {
         std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
 
@@ -195,23 +192,19 @@ namespace app {
         return mfccFeatureCalc;
     }
 
-    KWSPostProcess::KWSPostProcess(Classifier& classifier, Model* model,
+    KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
                                    const std::vector<std::string>& labels,
                                    std::vector<ClassificationResult>& results)
-            :m_kwsClassifier{classifier},
+            :m_outputTensor{outputTensor},
+             m_kwsClassifier{classifier},
              m_labels{labels},
              m_results{results}
-    {
-        if (!model->IsInited()) {
-            printf_err("Model is not initialised!.\n");
-        }
-        this->m_model = model;
-    }
+    {}
 
-    bool KWSPostProcess::DoPostProcess()
+    bool KwsPostProcess::DoPostProcess()
     {
         return this->m_kwsClassifier.GetClassificationResults(
-                this->m_model->GetOutputTensor(0), this->m_results,
+                this->m_outputTensor, this->m_results,
                 this->m_labels, 1, true);
     }
 
diff --git a/source/use_case/kws/src/UseCaseHandler.cc b/source/use_case/kws/src/UseCaseHandler.cc
index e73a2c3..61c6eb6 100644
--- a/source/use_case/kws/src/UseCaseHandler.cc
+++ b/source/use_case/kws/src/UseCaseHandler.cc
@@ -34,13 +34,12 @@ using KwsClassifier = arm::app::Classifier;
 namespace arm {
 namespace app {
 
-
     /**
      * @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);
+    static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results);
 
     /* KWS inference handler. */
     bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
@@ -50,6 +49,7 @@ namespace app {
         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")) {
@@ -61,16 +61,17 @@ namespace app {
         constexpr uint32_t dataPsnTxtInfStartX = 20;
         constexpr uint32_t dataPsnTxtInfStartY = 40;
         constexpr int minTensorDims = static_cast<int>(
-            (arm::app::MicroNetKwsModel::ms_inputRowsIdx > arm::app::MicroNetKwsModel::ms_inputColsIdx)?
-             arm::app::MicroNetKwsModel::ms_inputRowsIdx : arm::app::MicroNetKwsModel::ms_inputColsIdx);
-
+            (MicroNetKwsModel::ms_inputRowsIdx > MicroNetKwsModel::ms_inputColsIdx)?
+             MicroNetKwsModel::ms_inputRowsIdx : MicroNetKwsModel::ms_inputColsIdx);
 
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
         }
 
+        /* Get Input and Output tensors for pre/post processing. */
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -81,22 +82,23 @@ namespace app {
 
         /* Get input shape for feature extraction. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
-        const uint32_t numMfccFeatures = inputShape->data[arm::app::MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t numMfccFeatures = inputShape->data[MicroNetKwsModel::ms_inputColsIdx];
+        const uint32_t numMfccFrames = inputShape->data[arm::app::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 secondsPerSample = 1.0 / audio::MicroNetKwsMFCC::ms_defaultSamplingFreq;
 
         /* Set up pre and post-processing. */
-        KWSPreProcess preprocess = KWSPreProcess(&model, numMfccFeatures, mfccFrameLength, mfccFrameStride);
+        KwsPreProcess preProcess = KwsPreProcess(inputTensor, numMfccFeatures, numMfccFrames,
+                                                 mfccFrameLength, mfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
-        KWSPostProcess postprocess = KWSPostProcess(ctx.Get<KwsClassifier &>("classifier"), &model,
+        KwsPostProcess postProcess = KwsPostProcess(outputTensor, ctx.Get<KwsClassifier &>("classifier"),
                                                     ctx.Get<std::vector<std::string>&>("labels"),
                                                     singleInfResult);
 
-        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
-
+        /* Loop to process audio clips. */
         do {
             hal_lcd_clear(COLOR_BLACK);
 
@@ -106,7 +108,7 @@ namespace app {
             auto audioDataSlider = audio::SlidingWindow<const int16_t>(
                     get_audio_array(currentIndex),
                     get_audio_array_size(currentIndex),
-                    preprocess.m_audioDataWindowSize, preprocess.m_audioDataStride);
+                    preProcess.m_audioDataWindowSize, preProcess.m_audioDataStride);
 
             /* Declare a container to hold results from across the whole audio clip. */
             std::vector<kws::KwsResult> finalResults;
@@ -123,34 +125,34 @@ namespace app {
                 const int16_t* inferenceWindow = audioDataSlider.Next();
 
                 /* The first window does not have cache ready. */
-                preprocess.m_audioWindowIndex = audioDataSlider.Index();
+                preProcess.m_audioWindowIndex = audioDataSlider.Index();
 
                 info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                      audioDataSlider.TotalStrides() + 1);
 
                 /* Run the pre-processing, inference and post-processing. */
-                if (!runner.PreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+                if (!preProcess.DoPreProcess(inferenceWindow, audio::MicroNetKwsMFCC::ms_defaultSamplingFreq)) {
+                    printf_err("Pre-processing failed.");
                     return false;
                 }
 
-                profiler.StartProfiling("Inference");
-                if (!runner.RunInference()) {
+                if (!RunInference(model, profiler)) {
+                    printf_err("Inference failed.");
                     return false;
                 }
-                profiler.StopProfiling();
 
-                if (!runner.PostProcess()) {
+                if (!postProcess.DoPostProcess()) {
+                    printf_err("Post-processing failed.");
                     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() * secondsPerSample * preProcess.m_audioDataStride,
                         audioDataSlider.Index(), scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-                arm::app::DumpTensor(outputTensor);
+                DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
             } /* while (audioDataSlider.HasNext()) */
 
@@ -174,7 +176,7 @@ namespace app {
         return true;
     }
 
-    static bool PresentInferenceResult(const std::vector<arm::app::kws::KwsResult>& results)
+    static bool PresentInferenceResult(const std::vector<kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -187,7 +189,7 @@ namespace app {
         /* Display each result */
         uint32_t rowIdx1 = dataPsnTxtStartY1 + 2 * dataPsnTxtYIncr;
 
-        for (const auto & result : results) {
+        for (const auto& result : results) {
 
             std::string topKeyword{"<none>"};
             float score = 0.f;
diff --git a/source/use_case/vww/include/VisualWakeWordProcessing.hpp b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
index b1d68ce..bef161f 100644
--- a/source/use_case/vww/include/VisualWakeWordProcessing.hpp
+++ b/source/use_case/vww/include/VisualWakeWordProcessing.hpp
@@ -34,9 +34,9 @@ namespace app {
     public:
         /**
          * @brief       Constructor
-         * @param[in]   model   Pointer to the the Image classification Model object.
+         * @param[in]   inputTensor   Pointer to the TFLite Micro input Tensor.
          **/
-        explicit VisualWakeWordPreProcess(Model* model);
+        explicit VisualWakeWordPreProcess(TfLiteTensor* inputTensor);
 
         /**
          * @brief       Should perform pre-processing of 'raw' input image data and load it into
@@ -46,6 +46,9 @@ namespace app {
          * @return      true if successful, false otherwise.
          **/
         bool DoPreProcess(const void* input, size_t inputSize) override;
+
+    private:
+        TfLiteTensor* m_inputTensor;
     };
 
     /**
@@ -56,6 +59,7 @@ namespace app {
     class VisualWakeWordPostProcess : public BasePostProcess {
 
     private:
+        TfLiteTensor* m_outputTensor;
         Classifier& m_vwwClassifier;
         const std::vector<std::string>& m_labels;
         std::vector<ClassificationResult>& m_results;
@@ -63,19 +67,20 @@ namespace app {
     public:
         /**
          * @brief       Constructor
-         * @param[in]   classifier   Classifier object used to get top N results from classification.
-         * @param[in]   model        Pointer to the VWW classification Model object.
-         * @param[in]   labels       Vector of string labels to identify each output of the model.
-         * @param[out]  results      Vector of classification results to store decoded outputs.
+         * @param[in]   outputTensor   Pointer to the TFLite Micro output Tensor.
+         * @param[in]   classifier     Classifier object used to get top N results from classification.
+         * @param[in]   model          Pointer to the VWW classification Model object.
+         * @param[in]   labels         Vector of string labels to identify each output of the model.
+         * @param[out]  results        Vector of classification results to store decoded outputs.
          **/
-        VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+        VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
                 const std::vector<std::string>& labels,
                 std::vector<ClassificationResult>& results);
 
         /**
-         * @brief       Should perform post-processing of the result of inference then
-         *              populate classification result data for any later use.
-         * @return      true if successful, false otherwise.
+         * @brief    Should perform post-processing of the result of inference then
+         *           populate classification result data for any later use.
+         * @return   true if successful, false otherwise.
          **/
         bool DoPostProcess() override;
     };
diff --git a/source/use_case/vww/src/UseCaseHandler.cc b/source/use_case/vww/src/UseCaseHandler.cc
index 7681f89..267e6c4 100644
--- a/source/use_case/vww/src/UseCaseHandler.cc
+++ b/source/use_case/vww/src/UseCaseHandler.cc
@@ -53,7 +53,7 @@ namespace app {
         }
 
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
-
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
         if (!inputTensor->dims) {
             printf_err("Invalid input tensor dims\n");
             return false;
@@ -75,15 +75,13 @@ namespace app {
         const uint32_t displayChannels = 3;
 
         /* Set up pre and post-processing. */
-        VisualWakeWordPreProcess preprocess = VisualWakeWordPreProcess(&model);
+        VisualWakeWordPreProcess preProcess = VisualWakeWordPreProcess(inputTensor);
 
         std::vector<ClassificationResult> results;
-        VisualWakeWordPostProcess postprocess = VisualWakeWordPostProcess(
-                ctx.Get<Classifier&>("classifier"), &model,
+        VisualWakeWordPostProcess postProcess = VisualWakeWordPostProcess(outputTensor,
+                ctx.Get<Classifier&>("classifier"),
                 ctx.Get<std::vector<std::string>&>("labels"), results);
 
-        UseCaseRunner runner = UseCaseRunner(&preprocess, &postprocess, &model);
-
         do {
             hal_lcd_clear(COLOR_BLACK);
 
@@ -115,17 +113,18 @@ namespace app {
                                  inputTensor->bytes : IMAGE_DATA_SIZE;
 
             /* Run the pre-processing, inference and post-processing. */
-            if (!runner.PreProcess(imgSrc, imgSz)) {
+            if (!preProcess.DoPreProcess(imgSrc, imgSz)) {
+                printf_err("Pre-processing failed.");
                 return false;
             }
 
-            profiler.StartProfiling("Inference");
-            if (!runner.RunInference()) {
+            if (!RunInference(model, profiler)) {
+                printf_err("Inference failed.");
                 return false;
             }
-            profiler.StopProfiling();
 
-            if (!runner.PostProcess()) {
+            if (!postProcess.DoPostProcess()) {
+                printf_err("Post-processing failed.");
                 return false;
             }
 
@@ -138,7 +137,6 @@ namespace app {
             ctx.Set<std::vector<ClassificationResult>>("results", results);
 
 #if VERIFY_TEST_OUTPUT
-            TfLiteTensor* outputTensor = model.GetOutputTensor(0);
             arm::app::DumpTensor(outputTensor);
 #endif /* VERIFY_TEST_OUTPUT */
 
diff --git a/source/use_case/vww/src/VisualWakeWordProcessing.cc b/source/use_case/vww/src/VisualWakeWordProcessing.cc
index 94eae28..a9863c0 100644
--- a/source/use_case/vww/src/VisualWakeWordProcessing.cc
+++ b/source/use_case/vww/src/VisualWakeWordProcessing.cc
@@ -22,13 +22,9 @@
 namespace arm {
 namespace app {
 
-    VisualWakeWordPreProcess::VisualWakeWordPreProcess(Model* model)
-    {
-        if (!model->IsInited()) {
-            printf_err("Model is not initialised!.\n");
-        }
-        this->m_model = model;
-    }
+    VisualWakeWordPreProcess::VisualWakeWordPreProcess(TfLiteTensor* inputTensor)
+    :m_inputTensor{inputTensor}
+    {}
 
     bool VisualWakeWordPreProcess::DoPreProcess(const void* data, size_t inputSize)
     {
@@ -37,9 +33,8 @@ namespace app {
         }
 
         auto input = static_cast<const uint8_t*>(data);
-        TfLiteTensor* inputTensor = this->m_model->GetInputTensor(0);
 
-        auto unsignedDstPtr = static_cast<uint8_t*>(inputTensor->data.data);
+        auto unsignedDstPtr = static_cast<uint8_t*>(this->m_inputTensor->data.data);
 
         /* VWW model has one channel input => Convert image to grayscale here.
          * We expect images to always be RGB. */
@@ -47,10 +42,10 @@ namespace app {
 
         /* VWW model pre-processing is image conversion from uint8 to [0,1] float values,
          * then quantize them with input quantization info. */
-        QuantParams inQuantParams = GetTensorQuantParams(inputTensor);
+        QuantParams inQuantParams = GetTensorQuantParams(this->m_inputTensor);
 
-        auto signedDstPtr = static_cast<int8_t*>(inputTensor->data.data);
-        for (size_t i = 0; i < inputTensor->bytes; i++) {
+        auto signedDstPtr = static_cast<int8_t*>(this->m_inputTensor->data.data);
+        for (size_t i = 0; i < this->m_inputTensor->bytes; i++) {
             auto i_data_int8 = static_cast<int8_t>(
                     ((static_cast<float>(unsignedDstPtr[i]) / 255.0f) / inQuantParams.scale) + inQuantParams.offset
                     );
@@ -62,22 +57,18 @@ namespace app {
         return true;
     }
 
-    VisualWakeWordPostProcess::VisualWakeWordPostProcess(Classifier& classifier, Model* model,
+    VisualWakeWordPostProcess::VisualWakeWordPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
             const std::vector<std::string>& labels, std::vector<ClassificationResult>& results)
-            :m_vwwClassifier{classifier},
+            :m_outputTensor{outputTensor},
+             m_vwwClassifier{classifier},
              m_labels{labels},
              m_results{results}
-    {
-        if (!model->IsInited()) {
-            printf_err("Model is not initialised!.\n");
-        }
-        this->m_model = model;
-    }
+    {}
 
     bool VisualWakeWordPostProcess::DoPostProcess()
     {
         return this->m_vwwClassifier.GetClassificationResults(
-                this->m_model->GetOutputTensor(0), this->m_results,
+                this->m_outputTensor, this->m_results,
                 this->m_labels, 1, true);
     }
 
diff --git a/tests/use_case/asr/AsrFeaturesTests.cc b/tests/use_case/asr/AsrFeaturesTests.cc
index 6c23598..fe93c83 100644
--- a/tests/use_case/asr/AsrFeaturesTests.cc
+++ b/tests/use_case/asr/AsrFeaturesTests.cc
@@ -23,19 +23,19 @@
 #include <catch.hpp>
 #include <random>
 
-class TestPreprocess : public arm::app::ASRPreProcess {
+class TestPreprocess : public arm::app::AsrPreProcess {
 public:
 
     static bool ComputeDeltas(arm::app::Array2d<float>& mfcc,
                        arm::app::Array2d<float>& delta1,
                        arm::app::Array2d<float>& delta2)
     {
-        return ASRPreProcess::ComputeDeltas(mfcc, delta1, delta2);
+        return AsrPreProcess::ComputeDeltas(mfcc, delta1, delta2);
     }
 
     static void NormaliseVec(arm::app::Array2d<float>& vec)
     {
-        return ASRPreProcess::StandardizeVecF32(vec);
+        return AsrPreProcess::StandardizeVecF32(vec);
     }
 };
 
diff --git a/tests/use_case/asr/Wav2LetterPostprocessingTest.cc b/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
index d0b6505..11c4919 100644
--- a/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
+++ b/tests/use_case/asr/Wav2LetterPostprocessingTest.cc
@@ -24,9 +24,9 @@
 
 template <typename T>
 static TfLiteTensor GetTestTensor(
-                        std::vector <int>&      shape,
-                        T                       initVal,
-                        std::vector<T>&         vectorBuf)
+                        std::vector<int>&      shape,
+                        T                      initVal,
+                        std::vector<T>&        vectorBuf)
 {
     REQUIRE(0 != shape.size());
 
@@ -60,7 +60,7 @@ TEST_CASE("Checking return value")
         TfLiteTensor tensor = GetTestTensor<int8_t>(
                 tensorShape, 100, tensorVec);
 
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         REQUIRE(!post.DoPostProcess());
@@ -80,7 +80,7 @@ TEST_CASE("Checking return value")
         TfLiteTensor tensor = GetTestTensor<int8_t>(
                 tensorShape, 100, tensorVec);
 
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
@@ -110,7 +110,7 @@ TEST_CASE("Postprocessing - erasing required elements")
     {
         std::vector<int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(tensorShape, 100, tensorVec);
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
@@ -127,7 +127,7 @@ TEST_CASE("Postprocessing - erasing required elements")
         std::vector <int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
                 tensorShape, 100, tensorVec);
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
@@ -165,7 +165,7 @@ TEST_CASE("Postprocessing - erasing required elements")
         std::vector <int8_t> tensorVec;
         TfLiteTensor tensor = GetTestTensor<int8_t>(
                 tensorShape, 100, tensorVec);
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
@@ -208,7 +208,7 @@ TEST_CASE("Postprocessing - erasing required elements")
                 tensorShape, 100, tensorVec);
 
         /* Should not be able to erase the left context if it is the first iteration. */
-        arm::app::ASRPostProcess post{classifier, &tensor, dummyLabels, dummyResult, outputCtxLen,
+        arm::app::AsrPostProcess post{&tensor, classifier, dummyLabels, dummyResult, outputCtxLen,
                                       blankTokenIdx, arm::app::Wav2LetterModel::ms_outputRowsIdx};
 
         /* Copy elements to compare later. */
diff --git a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
index 0280af6..0a44093 100644
--- a/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
+++ b/tests/use_case/asr/Wav2LetterPreprocessingTest.cc
@@ -111,8 +111,8 @@ TEST_CASE("Preprocessing calculation INT8")
             tensorVec.data(), dims, quantScale, quantOffset, "preprocessedInput");
 
     /* Initialise pre-processing module. */
-    arm::app::ASRPreProcess prep{&inputTensor,
-        numMfccFeatures, numMfccVectors, mfccWindowLen, mfccWindowStride};
+    arm::app::AsrPreProcess prep{&inputTensor,
+                                 numMfccFeatures, numMfccVectors, mfccWindowLen, mfccWindowStride};
 
     /* Invoke pre-processing. */
     REQUIRE(prep.DoPreProcess(testWav.data(), testWav.size()));