diff --git a/source/use_case/kws_asr/include/AsrClassifier.hpp b/source/use_case/kws_asr/include/AsrClassifier.hpp
deleted file mode 100644
index 6ab9685..0000000
--- a/source/use_case/kws_asr/include/AsrClassifier.hpp
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef ASR_CLASSIFIER_HPP
-#define ASR_CLASSIFIER_HPP
-
-#include "Classifier.hpp"
-
-namespace arm {
-namespace app {
-
-    class AsrClassifier : public Classifier {
-    public:
-        /**
-         * @brief       Gets the top N classification results from the
-         *              output vector.
-         * @param[in]   outputTensor   Inference output tensor from an NN model.
-         * @param[out]  vecResults     A vector of classification results
-         *                             populated by this function.
-         * @param[in]   labels         Labels vector to match classified classes
-         * @param[in]   topNCount      Number of top classifications to pick.
-         * @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;
-
-    private:
-
-        /**
-         * @brief       Utility function that gets the top 1 classification results from the
-         *              output tensor (vector of vector).
-         * @param[in]   tensor       Inference output tensor from an NN model.
-         * @param[out]  vecResults   A vector of classification results
-         *                           populated by this function.
-         * @param[in]   labels       Labels vector to match classified classes.
-         * @param[in]   scale        Quantization scale.
-         * @param[in]   zeroPoint    Quantization zero point.
-         * @return      true if successful, false otherwise.
-         **/
-        template<typename T>
-        bool GetTopResults(TfLiteTensor* tensor,
-                           std::vector<ClassificationResult>& vecResults,
-                           const std::vector <std::string>& labels, double scale, double zeroPoint);
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* ASR_CLASSIFIER_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/AsrResult.hpp b/source/use_case/kws_asr/include/AsrResult.hpp
deleted file mode 100644
index 25fa9e8..0000000
--- a/source/use_case/kws_asr/include/AsrResult.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef ASR_RESULT_HPP
-#define ASR_RESULT_HPP
-
-#include "ClassificationResult.hpp"
-
-#include <vector>
-
-namespace arm {
-namespace app {
-namespace asr {
-
-    using ResultVec = std::vector<arm::app::ClassificationResult>;
-
-    /* Structure for holding asr result. */
-    class AsrResult {
-
-    public:
-        ResultVec       m_resultVec;        /* Container for "thresholded" classification results. */
-        float           m_timeStamp;        /* Audio timestamp for this result. */
-        uint32_t        m_inferenceNumber;  /* Corresponding inference number. */
-        float           m_threshold;        /* Threshold value for `m_resultVec` */
-
-        AsrResult() = delete;
-        AsrResult(ResultVec&        resultVec,
-                  const float       timestamp,
-                  const uint32_t    inferenceIdx,
-                  const float       scoreThreshold) {
-
-            this->m_threshold = scoreThreshold;
-            this->m_timeStamp = timestamp;
-            this->m_inferenceNumber = inferenceIdx;
-
-            this->m_resultVec = ResultVec();
-            for (auto& i : resultVec) {
-                if (i.m_normalisedVal >= this->m_threshold) {
-                    this->m_resultVec.emplace_back(i);
-                }
-            }
-        }
-        ~AsrResult() = default;
-    };
-
-} /* namespace asr */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* ASR_RESULT_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/KwsProcessing.hpp b/source/use_case/kws_asr/include/KwsProcessing.hpp
deleted file mode 100644
index d3de3b3..0000000
--- a/source/use_case/kws_asr/include/KwsProcessing.hpp
+++ /dev/null
@@ -1,138 +0,0 @@
-/*
- * Copyright (c) 2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_PROCESSING_HPP
-#define KWS_PROCESSING_HPP
-
-#include <AudioUtils.hpp>
-#include "BaseProcessing.hpp"
-#include "Model.hpp"
-#include "Classifier.hpp"
-#include "MicroNetKwsMfcc.hpp"
-
-#include <functional>
-
-namespace arm {
-namespace app {
-
-    /**
-     * @brief   Pre-processing class for Keyword Spotting use case.
-     *          Implements methods declared by BasePreProcess and anything else needed
-     *          to populate input tensors ready for inference.
-     */
-    class KwsPreProcess : public BasePreProcess {
-
-    public:
-        /**
-         * @brief       Constructor
-         * @param[in]   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(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
-         *              TFLite Micro input tensors ready for inference.
-         * @param[in]   input      Pointer to the data that pre-processing will work on.
-         * @param[in]   inputSize  Size of the input data.
-         * @return      true if successful, false otherwise.
-         **/
-        bool DoPreProcess(const void* input, size_t inputSize) override;
-
-        size_t m_audioWindowIndex = 0;  /* Index of audio slider, used when caching features in longer clips. */
-        size_t m_audioDataWindowSize;   /* Amount of audio needed for 1 inference. */
-        size_t m_audioDataStride;       /* Amount of audio to stride across if doing >1 inference in longer clips. */
-
-    private:
-        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;
-        size_t m_numMfccVectorsInAudioStride;
-        size_t m_numReusedMfccVectors;
-        std::function<void (std::vector<int16_t>&, int, bool, size_t)> m_mfccFeatureCalculator;
-
-        /**
-         * @brief Returns a function to perform feature calculation and populates input tensor data with
-         * MFCC data.
-         *
-         * Input tensor data type check is performed to choose correct MFCC feature data type.
-         * If tensor has an integer data type then original features are quantised.
-         *
-         * Warning: MFCC calculator provided as input must have the same life scope as returned function.
-         *
-         * @param[in]       mfcc          MFCC feature calculator.
-         * @param[in,out]   inputTensor   Input tensor pointer to store calculated features.
-         * @param[in]       cacheSize     Size of the feature vectors cache (number of feature vectors).
-         * @return          Function to be called providing audio sample and sliding window index.
-         */
-        std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-        GetFeatureCalculator(audio::MicroNetKwsMFCC&  mfcc,
-                             TfLiteTensor*            inputTensor,
-                             size_t                   cacheSize);
-
-        template<class T>
-        std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
-        FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                    std::function<std::vector<T> (std::vector<int16_t>& )> compute);
-    };
-
-    /**
-     * @brief   Post-processing class for Keyword Spotting use case.
-     *          Implements methods declared by BasePostProcess and anything else needed
-     *          to populate result vector.
-     */
-    class KwsPostProcess : public BasePostProcess {
-
-    private:
-        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]       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(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 KWS result data for any later use.
-         * @return   true if successful, false otherwise.
-         **/
-        bool DoPostProcess() override;
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_PROCESSING_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/KwsResult.hpp b/source/use_case/kws_asr/include/KwsResult.hpp
deleted file mode 100644
index 45bb790..0000000
--- a/source/use_case/kws_asr/include/KwsResult.hpp
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_RESULT_HPP
-#define KWS_RESULT_HPP
-
-#include "ClassificationResult.hpp"
-
-#include <vector>
-
-namespace arm {
-namespace app {
-namespace kws {
-
-    using ResultVec = std::vector < arm::app::ClassificationResult >;
-
-    /* Structure for holding kws result. */
-    class KwsResult {
-
-    public:
-        ResultVec       m_resultVec;        /* Container for "thresholded" classification results. */
-        float           m_timeStamp;        /* Audio timestamp for this result. */
-        uint32_t        m_inferenceNumber;  /* Corresponding inference number. */
-        float           m_threshold;        /* Threshold value for `m_resultVec.` */
-
-        KwsResult() = delete;
-        KwsResult(ResultVec&        resultVec,
-                  const float       timestamp,
-                  const uint32_t    inferenceIdx,
-                  const float       scoreThreshold) {
-
-            this->m_threshold = scoreThreshold;
-            this->m_timeStamp = timestamp;
-            this->m_inferenceNumber = inferenceIdx;
-
-            this->m_resultVec = ResultVec();
-            for (auto & i : resultVec) {
-                if (i.m_normalisedVal >= this->m_threshold) {
-                    this->m_resultVec.emplace_back(i);
-                }
-            }
-        }
-        ~KwsResult() = default;
-    };
-
-} /* namespace kws */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_RESULT_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp b/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp
deleted file mode 100644
index af6ba5f..0000000
--- a/source/use_case/kws_asr/include/MicroNetKwsMfcc.hpp
+++ /dev/null
@@ -1,51 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_MICRONET_MFCC_HPP
-#define KWS_ASR_MICRONET_MFCC_HPP
-
-#include "Mfcc.hpp"
-
-namespace arm {
-namespace app {
-namespace audio {
-
-    /* Class to provide MicroNet specific MFCC calculation requirements. */
-    class MicroNetKwsMFCC : public MFCC {
-
-    public:
-        static constexpr uint32_t  ms_defaultSamplingFreq = 16000;
-        static constexpr uint32_t  ms_defaultNumFbankBins =    40;
-        static constexpr uint32_t  ms_defaultMelLoFreq    =    20;
-        static constexpr uint32_t  ms_defaultMelHiFreq    =  4000;
-        static constexpr bool      ms_defaultUseHtkMethod =  true;
-
-
-        explicit MicroNetKwsMFCC(const size_t numFeats, const size_t frameLen)
-            :  MFCC(MfccParams(
-                        ms_defaultSamplingFreq, ms_defaultNumFbankBins,
-                        ms_defaultMelLoFreq, ms_defaultMelHiFreq,
-                        numFeats, frameLen, ms_defaultUseHtkMethod))
-        {}
-        MicroNetKwsMFCC()  = delete;
-        ~MicroNetKwsMFCC() = default;
-    };
-
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_MICRONET_MFCC_HPP */
diff --git a/source/use_case/kws_asr/include/MicroNetKwsModel.hpp b/source/use_case/kws_asr/include/MicroNetKwsModel.hpp
deleted file mode 100644
index 22cf916..0000000
--- a/source/use_case/kws_asr/include/MicroNetKwsModel.hpp
+++ /dev/null
@@ -1,66 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_MICRONETMODEL_HPP
-#define KWS_ASR_MICRONETMODEL_HPP
-
-#include "Model.hpp"
-
-namespace arm {
-namespace app {
-namespace kws {
-    extern const int g_FrameLength;
-    extern const int g_FrameStride;
-    extern const float g_ScoreThreshold;
-    extern const uint32_t g_NumMfcc;
-    extern const uint32_t g_NumAudioWins;
-} /* namespace kws */
-} /* namespace app */
-} /* namespace arm */
-
-namespace arm {
-namespace app {
-    class MicroNetKwsModel : public Model {
-    public:
-        /* Indices for the expected model - based on input and output tensor shapes */
-        static constexpr uint32_t ms_inputRowsIdx = 1;
-        static constexpr uint32_t ms_inputColsIdx = 2;
-        static constexpr uint32_t ms_outputRowsIdx = 2;
-        static constexpr uint32_t ms_outputColsIdx = 3;
-
-    protected:
-        /** @brief   Gets the reference to op resolver interface class. */
-        const tflite::MicroOpResolver& GetOpResolver() override;
-
-        /** @brief   Adds operations to the op resolver instance. */
-        bool EnlistOperations() override;
-
-        const uint8_t* ModelPointer() override;
-
-        size_t ModelSize() override;
-
-    private:
-        /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int ms_maxOpCnt = 7;
-
-        /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_MICRONETMODEL_HPP */
diff --git a/source/use_case/kws_asr/include/OutputDecode.hpp b/source/use_case/kws_asr/include/OutputDecode.hpp
deleted file mode 100644
index cea2c33..0000000
--- a/source/use_case/kws_asr/include/OutputDecode.hpp
+++ /dev/null
@@ -1,40 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_OUTPUT_DECODE_HPP
-#define KWS_ASR_OUTPUT_DECODE_HPP
-
-#include "AsrClassifier.hpp"
-
-namespace arm {
-namespace app {
-namespace audio {
-namespace asr {
-
-    /**
-     * @brief       Gets the top N classification results from the
-     *              output vector.
-     * @param[in]   vecResults   Label output from classifier.
-     * @return      true if successful, false otherwise.
-    **/
-    std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults);
-
-} /* namespace asr */
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_OUTPUT_DECODE_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp b/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
deleted file mode 100644
index 75d75da..0000000
--- a/source/use_case/kws_asr/include/Wav2LetterMfcc.hpp
+++ /dev/null
@@ -1,113 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_WAV2LET_MFCC_HPP
-#define KWS_ASR_WAV2LET_MFCC_HPP
-
-#include "Mfcc.hpp"
-
-namespace arm {
-namespace app {
-namespace audio {
-
-    /* Class to provide Wav2Letter specific MFCC calculation requirements. */
-    class Wav2LetterMFCC : public MFCC {
-
-    public:
-        static constexpr uint32_t  ms_defaultSamplingFreq = 16000;
-        static constexpr uint32_t  ms_defaultNumFbankBins =   128;
-        static constexpr uint32_t  ms_defaultMelLoFreq    =     0;
-        static constexpr uint32_t  ms_defaultMelHiFreq    =  8000;
-        static constexpr bool      ms_defaultUseHtkMethod = false;
-
-        explicit Wav2LetterMFCC(const size_t numFeats, const size_t frameLen)
-            :  MFCC(MfccParams(
-                        ms_defaultSamplingFreq, ms_defaultNumFbankBins,
-                        ms_defaultMelLoFreq, ms_defaultMelHiFreq,
-                        numFeats, frameLen, ms_defaultUseHtkMethod))
-        {}
-
-        Wav2LetterMFCC()  = delete;
-        ~Wav2LetterMFCC() = default;
-
-    protected:
-
-        /**
-         * @brief       Overrides base class implementation of this function.
-         * @param[in]   fftVec                  Vector populated with FFT magnitudes.
-         * @param[in]   melFilterBank           2D Vector with filter bank weights.
-         * @param[in]   filterBankFilterFirst   Vector containing the first indices of filter bank
-         *                                      to be used for each bin.
-         * @param[in]   filterBankFilterLast    Vector containing the last indices of filter bank
-         *                                      to be used for each bin.
-         * @param[out]  melEnergies             Pre-allocated vector of MEL energies to be
-         *                                      populated.
-         * @return      true if successful, false otherwise.
-         */
-        bool ApplyMelFilterBank(
-                std::vector<float>&                 fftVec,
-                std::vector<std::vector<float>>&    melFilterBank,
-                std::vector<uint32_t>&              filterBankFilterFirst,
-                std::vector<uint32_t>&              filterBankFilterLast,
-                std::vector<float>&                 melEnergies) override;
-
-        /**
-         * @brief           Override for the base class implementation convert mel
-         *                  energies to logarithmic scale. The difference from
-         *                  default behaviour is that the power is converted to dB
-         *                  and subsequently clamped.
-         * @param[in,out]   melEnergies   1D vector of Mel energies.
-         **/
-        void ConvertToLogarithmicScale(
-                std::vector<float>& melEnergies) override;
-
-        /**
-         * @brief       Create a matrix used to calculate Discrete Cosine
-         *              Transform. Override for the base class' default
-         *              implementation as the first and last elements
-         *              use a different normaliser.
-         * @param[in]   inputLength        Input length of the buffer on which
-         *                                 DCT will be performed.
-         * @param[in]   coefficientCount   Total coefficients per input length.
-         * @return      1D vector with inputLength x coefficientCount elements
-         *              populated with DCT coefficients.
-         */
-        std::vector<float> CreateDCTMatrix(
-                int32_t inputLength,
-                int32_t coefficientCount) override;
-
-        /**
-         * @brief       Given the low and high Mel values, get the normaliser
-         *              for weights to be applied when populating the filter
-         *              bank. Override for the base class implementation.
-         * @param[in]   leftMel        Low Mel frequency value.
-         * @param[in]   rightMel       High Mel frequency value.
-         * @param[in]   useHTKMethod   Bool to signal if HTK method is to be
-         *                             used for calculation.
-         * @return      Value to use for normalising.
-         */
-        float GetMelFilterBankNormaliser(
-                const float&   leftMel,
-                const float&   rightMel,
-                bool     useHTKMethod) override;
-
-    };
-
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_WAV2LET_MFCC_HPP */
diff --git a/source/use_case/kws_asr/include/Wav2LetterModel.hpp b/source/use_case/kws_asr/include/Wav2LetterModel.hpp
deleted file mode 100644
index 0e1adc5..0000000
--- a/source/use_case/kws_asr/include/Wav2LetterModel.hpp
+++ /dev/null
@@ -1,71 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_WAV2LETTER_MODEL_HPP
-#define KWS_ASR_WAV2LETTER_MODEL_HPP
-
-#include "Model.hpp"
-
-namespace arm {
-namespace app {
-namespace asr {
-    extern const int g_FrameLength;
-    extern const int g_FrameStride;
-    extern const float g_ScoreThreshold;
-    extern const int g_ctxLen;
-} /* namespace asr */
-} /* namespace app */
-} /* namespace arm */
-
-namespace arm {
-namespace app {
-
-    class Wav2LetterModel : public Model {
-
-    public:
-        /* Indices for the expected model - based on input and output tensor shapes */
-        static constexpr uint32_t ms_inputRowsIdx  = 1;
-        static constexpr uint32_t ms_inputColsIdx  = 2;
-        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;
-
-    protected:
-        /** @brief   Gets the reference to op resolver interface class. */
-        const tflite::MicroOpResolver& GetOpResolver() override;
-
-        /** @brief   Adds operations to the op resolver instance. */
-        bool EnlistOperations() override;
-
-        const uint8_t* ModelPointer() override;
-
-        size_t ModelSize() override;
-
-    private:
-        /* Maximum number of individual operations that can be enlisted. */
-        static constexpr int ms_maxOpCnt = 5;
-
-        /* A mutable op resolver instance. */
-        tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_WAV2LETTER_MODEL_HPP */
diff --git a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
deleted file mode 100644
index d1bc9a2..0000000
--- a/source/use_case/kws_asr/include/Wav2LetterPostprocess.hpp
+++ /dev/null
@@ -1,108 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_WAV2LETTER_POSTPROCESS_HPP
-#define KWS_ASR_WAV2LETTER_POSTPROCESS_HPP
-
-#include "TensorFlowLiteMicro.hpp"   /* TensorFlow headers. */
-#include "BaseProcessing.hpp"
-#include "AsrClassifier.hpp"
-#include "AsrResult.hpp"
-#include "log_macros.h"
-
-namespace arm {
-namespace app {
-
-    /**
-     * @brief   Helper class to manage tensor post-processing for "wav2letter"
-     *          output.
-     */
-    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 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]       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(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
-         *           populate ASR result data for any later use.
-         * @return   true if successful, false otherwise.
-         **/
-        bool DoPostProcess() override;
-
-        /** @brief   Gets the output inner length for post-processing. */
-        static uint32_t GetOutputInnerLen(const TfLiteTensor*, uint32_t outputCtxLen);
-
-        /** @brief   Gets the output context length (left/right) for post-processing. */
-        static uint32_t GetOutputContextLen(const Model& model, uint32_t inputCtxLen);
-
-        /** @brief   Gets the number of feature vectors to be computed. */
-        static uint32_t GetNumFeatureVectors(const Model& model);
-
-    private:
-        AsrClassifier& m_classifier;                /* ASR Classifier object. */
-        TfLiteTensor* m_outputTensor;               /* Model output tensor. */
-        const std::vector<std::string>& m_labels;   /* ASR Labels. */
-        asr::ResultVec & m_results;                 /* Results vector for a single inference. */
-        uint32_t m_outputContextLen;                /* lengths of left/right contexts for output. */
-        uint32_t m_outputInnerLen;                  /* Length of output inner context. */
-        uint32_t m_totalLen;                        /* Total length of the required axis. */
-        uint32_t m_countIterations;                 /* Current number of iterations. */
-        uint32_t m_blankTokenIdx;                   /* Index of the labels blank token. */
-        uint32_t m_reductionAxisIdx;                /* Axis containing output logits for a single step. */
-
-        /**
-         * @brief    Checks if the tensor and axis index are valid
-         *           inputs to the object - based on how it has been initialised.
-         * @return   true if valid, false otherwise.
-         */
-        bool IsInputValid(TfLiteTensor*  tensor,
-                          uint32_t axisIdx) const;
-
-        /**
-         * @brief    Gets the tensor data element size in bytes based
-         *           on the tensor type.
-         * @return   Size in bytes, 0 if not supported.
-         */
-        static uint32_t GetTensorElementSize(TfLiteTensor* tensor);
-
-        /**
-         * @brief    Erases sections from the data assuming row-wise
-         *           arrangement along the context axis.
-         * @return   true if successful, false otherwise.
-         */
-        bool EraseSectionsRowWise(uint8_t* ptrData,
-                                  uint32_t strideSzBytes,
-                                  bool lastIteration);
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_WAV2LETTER_POSTPROCESS_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp b/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
deleted file mode 100644
index 1224c23..0000000
--- a/source/use_case/kws_asr/include/Wav2LetterPreprocess.hpp
+++ /dev/null
@@ -1,182 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#ifndef KWS_ASR_WAV2LETTER_PREPROCESS_HPP
-#define KWS_ASR_WAV2LETTER_PREPROCESS_HPP
-
-#include "Wav2LetterModel.hpp"
-#include "Wav2LetterMfcc.hpp"
-#include "AudioUtils.hpp"
-#include "DataStructures.hpp"
-#include "BaseProcessing.hpp"
-#include "log_macros.h"
-
-namespace arm {
-namespace app {
-
-    /* Class to facilitate pre-processing calculation for Wav2Letter model
-     * for ASR. */
-    using AudioWindow = audio::SlidingWindow<const int16_t>;
-
-    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.
-         */
-        AsrPreProcess(TfLiteTensor* inputTensor,
-                      uint32_t  numMfccFeatures,
-                      uint32_t  numFeatureFrames,
-                      uint32_t  mfccWindowLen,
-                      uint32_t  mfccWindowStride);
-
-        /**
-         * @brief       Calculates the features required from audio data. This
-         *              includes MFCC, first and second order deltas,
-         *              normalisation and finally, quantisation. The tensor is
-         *              populated with features from a given window placed along
-         *              in a single row.
-         * @param[in]   audioData      Pointer to the first element of audio data.
-         * @param[in]   audioDataLen   Number of elements in the audio data.
-         * @return      true if successful, false in case of error.
-         */
-        bool DoPreProcess(const void* audioData, size_t audioDataLen) override;
-
-    protected:
-         /**
-          * @brief Computes the first and second order deltas for the
-          *        MFCC buffers - they are assumed to be populated.
-          *
-          * @param[in]  mfcc     MFCC buffers.
-          * @param[out] delta1   Result of the first diff computation.
-          * @param[out] delta2   Result of the second diff computation.
-          * @return     true if successful, false otherwise.
-          */
-         static bool ComputeDeltas(Array2d<float>& mfcc,
-                                   Array2d<float>& delta1,
-                                   Array2d<float>& delta2);
-
-        /**
-         * @brief           Given a 2D vector of floats, rescale it to have mean of 0 and
-        *                   standard deviation of 1.
-         * @param[in,out]   vec   Vector of vector of floats.
-         */
-        static void StandardizeVecF32(Array2d<float>& vec);
-
-        /**
-         * @brief   Standardizes all the MFCC and delta buffers to have mean 0 and std. dev 1.
-         */
-        void Standarize();
-
-        /**
-         * @brief       Given the quantisation and data type limits, computes
-         *              the quantised values of a floating point input data.
-         * @param[in]   elem          Element to be quantised.
-         * @param[in]   quantScale    Scale.
-         * @param[in]   quantOffset   Offset.
-         * @param[in]   minVal        Numerical limit - minimum.
-         * @param[in]   maxVal        Numerical limit - maximum.
-         * @return      Floating point quantised value.
-         */
-        static float GetQuantElem(
-                float     elem,
-                float     quantScale,
-                int       quantOffset,
-                float     minVal,
-                float     maxVal);
-
-        /**
-         * @brief       Quantises the MFCC and delta buffers, and places them
-         *              in the output buffer. While doing so, it transposes
-         *              the data. Reason: Buffers in this class are arranged
-         *              for "time" axis to be row major. Primary reason for
-         *              this being the convolution speed up (as we can use
-         *              contiguous memory). The output, however, requires the
-         *              time axis to be in column major arrangement.
-         * @param[in]   outputBuf     Pointer to the output buffer.
-         * @param[in]   outputBufSz   Output buffer's size.
-         * @param[in]   quantScale    Quantisation scale.
-         * @param[in]   quantOffset   Quantisation offset.
-         */
-        template <typename T>
-        bool Quantise(
-                T*              outputBuf,
-                const uint32_t  outputBufSz,
-                const float     quantScale,
-                const int       quantOffset)
-        {
-            /* Check the output size will fit everything. */
-            if (outputBufSz < (this->m_mfccBuf.size(0) * 3 * sizeof(T))) {
-                printf_err("Tensor size too small for features\n");
-                return false;
-            }
-
-            /* Populate. */
-            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();
-            const float maxVal = std::numeric_limits<T>::max();
-
-            /* 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(
-                            this->m_mfccBuf(i, j), quantScale,
-                            quantOffset, minVal, maxVal));
-                    *outputBufD1++ = static_cast<T>(AsrPreProcess::GetQuantElem(
-                            this->m_delta1Buf(i, j), quantScale,
-                            quantOffset, minVal, maxVal));
-                    *outputBufD2++ = static_cast<T>(AsrPreProcess::GetQuantElem(
-                            this->m_delta2Buf(i, j), quantScale,
-                            quantOffset, minVal, maxVal));
-                }
-                outputBufMfcc += ptrIncr;
-                outputBufD1 += ptrIncr;
-                outputBufD2 += ptrIncr;
-            }
-
-            return true;
-        }
-
-    private:
-        audio::Wav2LetterMFCC   m_mfcc;          /* MFCC instance. */
-        TfLiteTensor*           m_inputTensor;   /* Model input tensor. */
-
-        /* Actual buffers to be populated. */
-        Array2d<float>   m_mfccBuf;              /* Contiguous buffer 1D: MFCC */
-        Array2d<float>   m_delta1Buf;            /* Contiguous buffer 1D: Delta 1 */
-        Array2d<float>   m_delta2Buf;            /* Contiguous buffer 1D: Delta 2 */
-
-        uint32_t         m_mfccWindowLen;        /* Window length for MFCC. */
-        uint32_t         m_mfccWindowStride;     /* Window stride len for MFCC. */
-        uint32_t         m_numMfccFeats;         /* Number of MFCC features per window. */
-        uint32_t         m_numFeatureFrames;     /* How many sets of m_numMfccFeats. */
-        AudioWindow      m_mfccSlidingWindow;    /* Sliding window to calculate MFCCs. */
-
-    };
-
-} /* namespace app */
-} /* namespace arm */
-
-#endif /* KWS_ASR_WAV2LETTER_PREPROCESS_HPP */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc
deleted file mode 100644
index 9c18b14..0000000
--- a/source/use_case/kws_asr/src/AsrClassifier.cc
+++ /dev/null
@@ -1,136 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "AsrClassifier.hpp"
-
-#include "log_macros.h"
-#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;
-    }
-
-    /* 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);
-
-    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);
-
-        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;
-    }
-
-    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(
-            TfLiteTensor* outputTensor,
-            std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
-{
-        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 uint32_t outColsIdx = arm::app::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 3D (1, m, n)\n");
-            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");
-        }
-
-        /* 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;
-        }
-
-        return true;
-}
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/KwsProcessing.cc b/source/use_case/kws_asr/src/KwsProcessing.cc
deleted file mode 100644
index 328709d..0000000
--- a/source/use_case/kws_asr/src/KwsProcessing.cc
+++ /dev/null
@@ -1,212 +0,0 @@
-/*
- * Copyright (c) 2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "KwsProcessing.hpp"
-#include "ImageUtils.hpp"
-#include "log_macros.h"
-#include "MicroNetKwsModel.hpp"
-
-namespace arm {
-namespace app {
-
-    KwsPreProcess::KwsPreProcess(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)}
-    {
-        this->m_mfcc.Init();
-
-        /* Deduce the data length required for 1 inference from the network parameters. */
-        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. */
-        this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(nullptr, this->m_audioDataWindowSize,
-                this->m_mfccFrameLength, this->m_mfccFrameStride);
-
-        /* For longer audio clips we choose to move by half the audio window size
-         * => for a 1 second window size there is an overlap of 0.5 seconds. */
-        this->m_audioDataStride = this->m_audioDataWindowSize / 2;
-
-        /* To have the previously calculated features re-usable, stride must be multiple
-         * of MFCC features window stride. Reduce stride through audio if needed. */
-        if (0 != this->m_audioDataStride % this->m_mfccFrameStride) {
-            this->m_audioDataStride -= this->m_audioDataStride % this->m_mfccFrameStride;
-        }
-
-        this->m_numMfccVectorsInAudioStride = this->m_audioDataStride / this->m_mfccFrameStride;
-
-        /* Calculate number of the feature vectors in the window overlap region.
-         * These feature vectors will be reused.*/
-        this->m_numReusedMfccVectors = this->m_mfccSlidingWindow.TotalStrides() + 1
-                - this->m_numMfccVectorsInAudioStride;
-
-        /* Construct feature calculation function. */
-        this->m_mfccFeatureCalculator = GetFeatureCalculator(this->m_mfcc, this->m_inputTensor,
-                                                             this->m_numReusedMfccVectors);
-
-        if (!this->m_mfccFeatureCalculator) {
-            printf_err("Feature calculator not initialized.");
-        }
-    }
-
-    bool KwsPreProcess::DoPreProcess(const void* data, size_t inputSize)
-    {
-        UNUSED(inputSize);
-        if (data == nullptr) {
-            printf_err("Data pointer is null");
-        }
-
-        /* Set the features sliding window to the new address. */
-        auto input = static_cast<const int16_t*>(data);
-        this->m_mfccSlidingWindow.Reset(input);
-
-        /* Cache is only usable if we have more than 1 inference in an audio clip. */
-        bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedMfccVectors > 0;
-
-        /* Use a sliding window to calculate MFCC features frame by frame. */
-        while (this->m_mfccSlidingWindow.HasNext()) {
-            const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
-
-            std::vector<int16_t> mfccFrameAudioData = std::vector<int16_t>(mfccWindow,
-                    mfccWindow + this->m_mfccFrameLength);
-
-            /* Compute features for this window and write them to input tensor. */
-            this->m_mfccFeatureCalculator(mfccFrameAudioData, this->m_mfccSlidingWindow.Index(),
-                                          useCache, this->m_numMfccVectorsInAudioStride);
-        }
-
-        debug("Input tensor populated \n");
-
-        return true;
-    }
-
-    /**
-     * @brief Generic feature calculator factory.
-     *
-     * Returns lambda function to compute features using features cache.
-     * Real features math is done by a lambda function provided as a parameter.
-     * Features are written to input tensor memory.
-     *
-     * @tparam T                Feature vector type.
-     * @param[in] inputTensor   Model input tensor pointer.
-     * @param[in] cacheSize     Number of feature vectors to cache. Defined by the sliding window overlap.
-     * @param[in] compute       Features calculator function.
-     * @return                  Lambda function to compute features.
-     */
-    template<class T>
-    std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
-    KwsPreProcess::FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                               std::function<std::vector<T> (std::vector<int16_t>& )> compute)
-    {
-        /* Feature cache to be captured by lambda function. */
-        static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
-
-        return [=](std::vector<int16_t>& audioDataWindow,
-                   size_t index,
-                   bool useCache,
-                   size_t featuresOverlapIndex)
-        {
-            T* tensorData = tflite::GetTensorData<T>(inputTensor);
-            std::vector<T> features;
-
-            /* Reuse features from cache if cache is ready and sliding windows overlap.
-             * Overlap is in the beginning of sliding window with a size of a feature cache. */
-            if (useCache && index < featureCache.size()) {
-                features = std::move(featureCache[index]);
-            } else {
-                features = std::move(compute(audioDataWindow));
-            }
-            auto size = features.size();
-            auto sizeBytes = sizeof(T) * size;
-            std::memcpy(tensorData + (index * size), features.data(), sizeBytes);
-
-            /* Start renewing cache as soon iteration goes out of the windows overlap. */
-            if (index >= featuresOverlapIndex) {
-                featureCache[index - featuresOverlapIndex] = std::move(features);
-            }
-        };
-    }
-
-    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    KwsPreProcess::FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
-                                       size_t cacheSize,
-                                       std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
-
-    template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
-    KwsPreProcess::FeatureCalc<float>(TfLiteTensor* inputTensor,
-                                      size_t cacheSize,
-                                      std::function<std::vector<float>(std::vector<int16_t>&)> compute);
-
-
-    std::function<void (std::vector<int16_t>&, int, bool, size_t)>
-    KwsPreProcess::GetFeatureCalculator(audio::MicroNetKwsMFCC& mfcc, TfLiteTensor* inputTensor, size_t cacheSize)
-    {
-        std::function<void (std::vector<int16_t>&, size_t, bool, size_t)> mfccFeatureCalc;
-
-        TfLiteQuantization quant = inputTensor->quantization;
-
-        if (kTfLiteAffineQuantization == quant.type) {
-            auto *quantParams = (TfLiteAffineQuantization *) quant.params;
-            const float quantScale = quantParams->scale->data[0];
-            const int quantOffset = quantParams->zero_point->data[0];
-
-            switch (inputTensor->type) {
-                case kTfLiteInt8: {
-                    mfccFeatureCalc = this->FeatureCalc<int8_t>(inputTensor,
-                                                          cacheSize,
-                                                          [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                              return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
-                                                                                                   quantScale,
-                                                                                                   quantOffset);
-                                                          }
-                    );
-                    break;
-                }
-                default:
-                printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
-            }
-        } else {
-            mfccFeatureCalc = this->FeatureCalc<float>(inputTensor, cacheSize,
-                    [&mfcc](std::vector<int16_t>& audioDataWindow) {
-                return mfcc.MfccCompute(audioDataWindow); }
-                );
-        }
-        return mfccFeatureCalc;
-    }
-
-    KwsPostProcess::KwsPostProcess(TfLiteTensor* outputTensor, Classifier& classifier,
-                                   const std::vector<std::string>& labels,
-                                   std::vector<ClassificationResult>& results)
-            :m_outputTensor{outputTensor},
-             m_kwsClassifier{classifier},
-             m_labels{labels},
-             m_results{results}
-    {}
-
-    bool KwsPostProcess::DoPostProcess()
-    {
-        return this->m_kwsClassifier.GetClassificationResults(
-                this->m_outputTensor, this->m_results,
-                this->m_labels, 1, true);
-    }
-
-} /* namespace app */
-} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/MainLoop.cc b/source/use_case/kws_asr/src/MainLoop.cc
index f1d97a0..2365264 100644
--- a/source/use_case/kws_asr/src/MainLoop.cc
+++ b/source/use_case/kws_asr/src/MainLoop.cc
@@ -23,7 +23,24 @@
 #include "Wav2LetterModel.hpp"      /* ASR model class for running inference. */
 #include "UseCaseCommonUtils.hpp"   /* Utils functions. */
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
-#include "log_macros.h"
+#include "log_macros.h"             /* Logging functions */
+#include "BufAttributes.hpp"        /* Buffer attributes to be applied */
+
+namespace arm {
+namespace app {
+    static uint8_t  tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+
+    namespace asr {
+        extern uint8_t* GetModelPointer();
+        extern size_t GetModelLen();
+    }
+
+    namespace kws {
+        extern uint8_t* GetModelPointer();
+        extern size_t GetModelLen();
+    }
+} /* namespace app */
+} /* namespace arm */
 
 using KwsClassifier = arm::app::Classifier;
 
@@ -60,14 +77,29 @@
     arm::app::Wav2LetterModel asrModel;
 
     /* Load the models. */
-    if (!kwsModel.Init()) {
+    if (!kwsModel.Init(arm::app::tensorArena,
+                       sizeof(arm::app::tensorArena),
+                       arm::app::kws::GetModelPointer(),
+                       arm::app::kws::GetModelLen())) {
         printf_err("Failed to initialise KWS model\n");
         return;
     }
 
+#if !defined(ARM_NPU)
+    /* If it is not a NPU build check if the model contains a NPU operator */
+    if (kwsModel.ContainsEthosUOperator()) {
+        printf_err("No driver support for Ethos-U operator found in the KWS model.\n");
+        return;
+    }
+#endif /* ARM_NPU */
+
     /* Initialise the asr model using the same allocator from KWS
      * to re-use the tensor arena. */
-    if (!asrModel.Init(kwsModel.GetAllocator())) {
+    if (!asrModel.Init(arm::app::tensorArena,
+                       sizeof(arm::app::tensorArena),
+                       arm::app::asr::GetModelPointer(),
+                       arm::app::asr::GetModelLen(),
+                       kwsModel.GetAllocator())) {
         printf_err("Failed to initialise ASR model\n");
         return;
     } else if (!VerifyTensorDimensions(asrModel)) {
@@ -75,6 +107,14 @@
         return;
     }
 
+#if !defined(ARM_NPU)
+    /* If it is not a NPU build check if the model contains a NPU operator */
+    if (asrModel.ContainsEthosUOperator()) {
+        printf_err("No driver support for Ethos-U operator found in the ASR model.\n");
+        return;
+    }
+#endif /* ARM_NPU */
+
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
diff --git a/source/use_case/kws_asr/src/MicroNetKwsModel.cc b/source/use_case/kws_asr/src/MicroNetKwsModel.cc
deleted file mode 100644
index 663faa0..0000000
--- a/source/use_case/kws_asr/src/MicroNetKwsModel.cc
+++ /dev/null
@@ -1,63 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "MicroNetKwsModel.hpp"
-#include "log_macros.h"
-
-namespace arm {
-namespace app {
-namespace kws {
-    extern uint8_t* GetModelPointer();
-    extern size_t GetModelLen();
-} /* namespace kws */
-} /* namespace app */
-} /* namespace arm */
-
-const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver()
-{
-    return this->m_opResolver;
-}
-
-bool arm::app::MicroNetKwsModel::EnlistOperations()
-{
-    this->m_opResolver.AddAveragePool2D();
-    this->m_opResolver.AddConv2D();
-    this->m_opResolver.AddDepthwiseConv2D();
-    this->m_opResolver.AddFullyConnected();
-    this->m_opResolver.AddRelu();
-    this->m_opResolver.AddReshape();
-
-#if defined(ARM_NPU)
-    if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
-        info("Added %s support to op resolver\n",
-            tflite::GetString_ETHOSU());
-    } else {
-        printf_err("Failed to add Arm NPU support to op resolver.");
-        return false;
-    }
-#endif /* ARM_NPU */
-    return true;
-}
-
-const uint8_t* arm::app::MicroNetKwsModel::ModelPointer()
-{
-    return arm::app::kws::GetModelPointer();
-}
-
-size_t arm::app::MicroNetKwsModel::ModelSize()
-{
-    return arm::app::kws::GetModelLen();
-}
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/OutputDecode.cc b/source/use_case/kws_asr/src/OutputDecode.cc
deleted file mode 100644
index 41fbe07..0000000
--- a/source/use_case/kws_asr/src/OutputDecode.cc
+++ /dev/null
@@ -1,47 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "OutputDecode.hpp"
-
-namespace arm {
-namespace app {
-namespace audio {
-namespace asr {
-
-    std::string DecodeOutput(const std::vector<ClassificationResult>& vecResults)
-    {
-        std::string CleanOutputBuffer;
-
-        for (size_t i = 0; i < vecResults.size(); ++i)  /* For all elements in vector. */
-        {
-            while (i+1 < vecResults.size() &&
-                   vecResults[i].m_label == vecResults[i+1].m_label)  /* While the current element is equal to the next, ignore it and move on. */
-            {
-                ++i;
-            }
-            if (vecResults[i].m_label != "$")  /* $ is a character used to represent unknown and double characters so should not be in output. */
-            {
-                CleanOutputBuffer += vecResults[i].m_label;  /* If the element is different to the next, it will be appended to CleanOutputBuffer. */
-            }
-        }
-
-        return CleanOutputBuffer;  /* Return string type containing clean output. */
-    }
-
-} /* namespace asr */
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index 01aefae..9427ae0 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -25,6 +25,7 @@
 #include "MicroNetKwsMfcc.hpp"
 #include "Classifier.hpp"
 #include "KwsResult.hpp"
+#include "Wav2LetterModel.hpp"
 #include "Wav2LetterMfcc.hpp"
 #include "Wav2LetterPreprocess.hpp"
 #include "Wav2LetterPostprocess.hpp"
@@ -470,4 +471,4 @@
     }
 
 } /* namespace app */
-} /* namespace arm */
\ No newline at end of file
+} /* namespace arm */
diff --git a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
deleted file mode 100644
index f2c50f3..0000000
--- a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "Wav2LetterMfcc.hpp"
-
-#include "PlatformMath.hpp"
-#include "log_macros.h"
-
-#include <cfloat>
-
-namespace arm {
-namespace app {
-namespace audio {
-
-    bool Wav2LetterMFCC::ApplyMelFilterBank(
-            std::vector<float>&                 fftVec,
-            std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<uint32_t>&              filterBankFilterFirst,
-            std::vector<uint32_t>&              filterBankFilterLast,
-            std::vector<float>&                 melEnergies)
-    {
-        const size_t numBanks = melEnergies.size();
-
-        if (numBanks != filterBankFilterFirst.size() ||
-                numBanks != filterBankFilterLast.size()) {
-            printf_err("unexpected filter bank lengths\n");
-            return false;
-        }
-
-        for (size_t bin = 0; bin < numBanks; ++bin) {
-            auto filterBankIter = melFilterBank[bin].begin();
-            auto end = melFilterBank[bin].end();
-            /* Avoid log of zero at later stages, same value used in librosa.
-             * The number was used during our default wav2letter model training. */
-            float melEnergy = 1e-10;
-            const uint32_t firstIndex = filterBankFilterFirst[bin];
-            const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
-
-            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
-                melEnergy += (*filterBankIter++ * fftVec[i]);
-            }
-
-            melEnergies[bin] = melEnergy;
-        }
-
-        return true;
-    }
-
-    void Wav2LetterMFCC::ConvertToLogarithmicScale(
-                            std::vector<float>& melEnergies)
-    {
-        float maxMelEnergy = -FLT_MAX;
-
-        /* Container for natural logarithms of mel energies. */
-        std::vector <float> vecLogEnergies(melEnergies.size(), 0.f);
-
-        /* Because we are taking natural logs, we need to multiply by log10(e).
-         * Also, for wav2letter model, we scale our log10 values by 10. */
-        constexpr float multiplier = 10.0 *  /* Default scalar. */
-                                      0.4342944819032518;  /* log10f(std::exp(1.0))*/
-
-        /* Take log of the whole vector. */
-        math::MathUtils::VecLogarithmF32(melEnergies, vecLogEnergies);
-
-        /* Scale the log values and get the max. */
-        for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
-                  iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
-
-            *iterM = *iterL * multiplier;
-
-            /* Save the max mel energy. */
-            if (*iterM > maxMelEnergy) {
-                maxMelEnergy = *iterM;
-            }
-        }
-
-        /* Clamp the mel energies. */
-        constexpr float maxDb = 80.0;
-        const float clampLevelLowdB = maxMelEnergy - maxDb;
-        for (float & melEnergie : melEnergies) {
-            melEnergie = std::max(melEnergie, clampLevelLowdB);
-        }
-    }
-
-    std::vector<float> Wav2LetterMFCC::CreateDCTMatrix(
-                                        const int32_t inputLength,
-                                        const int32_t coefficientCount)
-    {
-        std::vector<float> dctMatix(inputLength * coefficientCount);
-
-        /* Orthonormal normalization. */
-        const float normalizerK0 = 2 * math::MathUtils::SqrtF32(1.0f /
-                                        static_cast<float>(4*inputLength));
-        const float normalizer = 2 * math::MathUtils::SqrtF32(1.0f /
-                                        static_cast<float>(2*inputLength));
-
-        const float angleIncr = M_PI/inputLength;
-        float angle = angleIncr;  /* We start using it at k = 1 loop. */
-
-        /* First row of DCT will use normalizer K0 */
-        for (int32_t n = 0; n < inputLength; ++n) {
-            dctMatix[n] = normalizerK0  /* cos(0) = 1 */;
-        }
-
-        /* Second row (index = 1) onwards, we use standard normalizer. */
-        for (int32_t k = 1, m = inputLength; k < coefficientCount; ++k, m += inputLength) {
-            for (int32_t n = 0; n < inputLength; ++n) {
-                dctMatix[m+n] = normalizer *
-                    math::MathUtils::CosineF32((n + 0.5f) * angle);
-            }
-            angle += angleIncr;
-        }
-        return dctMatix;
-    }
-
-    float Wav2LetterMFCC::GetMelFilterBankNormaliser(
-                                    const float&    leftMel,
-                                    const float&    rightMel,
-                                    const bool      useHTKMethod)
-    {
-        /* Slaney normalization for mel weights. */
-        return (2.0f / (MFCC::InverseMelScale(rightMel, useHTKMethod) -
-                MFCC::InverseMelScale(leftMel, useHTKMethod)));
-    }
-
-} /* namespace audio */
-} /* namespace app */
-} /* namespace arm */
diff --git a/source/use_case/kws_asr/src/Wav2LetterModel.cc b/source/use_case/kws_asr/src/Wav2LetterModel.cc
deleted file mode 100644
index 52bd23a..0000000
--- a/source/use_case/kws_asr/src/Wav2LetterModel.cc
+++ /dev/null
@@ -1,61 +0,0 @@
-/*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "Wav2LetterModel.hpp"
-#include "log_macros.h"
-
-namespace arm {
-namespace app {
-namespace asr {
-    extern uint8_t* GetModelPointer();
-    extern size_t GetModelLen();
-}
-} /* namespace app */
-} /* namespace arm */
-
-const tflite::MicroOpResolver& arm::app::Wav2LetterModel::GetOpResolver()
-{
-    return this->m_opResolver;
-}
-
-bool arm::app::Wav2LetterModel::EnlistOperations()
-{
-    this->m_opResolver.AddConv2D();
-    this->m_opResolver.AddLeakyRelu();
-    this->m_opResolver.AddSoftmax();
-    this->m_opResolver.AddReshape();
-
-#if defined(ARM_NPU)
-    if (kTfLiteOk == this->m_opResolver.AddEthosU()) {
-        info("Added %s support to op resolver\n",
-            tflite::GetString_ETHOSU());
-    } else {
-        printf_err("Failed to add Arm NPU support to op resolver.");
-        return false;
-    }
-#endif /* ARM_NPU */
-    return true;
-}
-
-const uint8_t* arm::app::Wav2LetterModel::ModelPointer()
-{
-    return arm::app::asr::GetModelPointer();
-}
-
-size_t arm::app::Wav2LetterModel::ModelSize()
-{
-    return arm::app::asr::GetModelLen();
-}
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
deleted file mode 100644
index 42f434e..0000000
--- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
+++ /dev/null
@@ -1,214 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "Wav2LetterPostprocess.hpp"
-
-#include "Wav2LetterModel.hpp"
-#include "log_macros.h"
-
-#include <cmath>
-
-namespace arm {
-namespace app {
-
-    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
-            ):
-            m_classifier(classifier),
-            m_outputTensor(outputTensor),
-            m_labels{labels},
-            m_results(results),
-            m_outputContextLen(outputContextLen),
-            m_countIterations(0),
-            m_blankTokenIdx(blankTokenIdx),
-            m_reductionAxisIdx(reductionAxisIdx)
-    {
-        this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
-        this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen);
-    }
-
-    bool AsrPostProcess::DoPostProcess()
-    {
-        /* Basic checks. */
-        if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) {
-            return false;
-        }
-
-        /* 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);
-
-        /* Other sanity checks. */
-        if (0 == elemSz) {
-            printf_err("Tensor type not supported for post processing\n");
-            return false;
-        } else if (elemSz * this->m_totalLen > this->m_outputTensor->bytes) {
-            printf_err("Insufficient number of tensor bytes\n");
-            return false;
-        }
-
-        /* Which axis do we need to process? */
-        switch (this->m_reductionAxisIdx) {
-            case Wav2LetterModel::ms_outputRowsIdx:
-                this->EraseSectionsRowWise(
-                        ptrData, elemSz * this->m_outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx],
-                        this->m_lastIteration);
-                break;
-            default:
-                printf_err("Unsupported axis index: %" PRIu32 "\n", this->m_reductionAxisIdx);
-                return false;
-        }
-        this->m_classifier.GetClassificationResults(this->m_outputTensor,
-                this->m_results, this->m_labels, 1);
-
-        return true;
-    }
-
-    bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
-    {
-        if (nullptr == tensor) {
-            return false;
-        }
-
-        if (static_cast<int>(axisIdx) >= tensor->dims->size) {
-            printf_err("Invalid axis index: %" PRIu32 "; Max: %d\n",
-                axisIdx, tensor->dims->size);
-            return false;
-        }
-
-        if (static_cast<int>(this->m_totalLen) !=
-                             tensor->dims->data[axisIdx]) {
-            printf_err("Unexpected tensor dimension for axis %d, got %d, \n",
-                axisIdx, tensor->dims->data[axisIdx]);
-            return false;
-        }
-
-        return true;
-    }
-
-    uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
-    {
-        switch(tensor->type) {
-            case kTfLiteUInt8:
-            case kTfLiteInt8:
-                return 1;
-            case kTfLiteInt16:
-                return 2;
-            case kTfLiteInt32:
-            case kTfLiteFloat32:
-                return 4;
-            default:
-                printf_err("Unsupported tensor type %s\n",
-                    TfLiteTypeGetName(tensor->type));
-        }
-
-        return 0;
-    }
-
-    bool AsrPostProcess::EraseSectionsRowWise(
-            uint8_t*         ptrData,
-            const uint32_t   strideSzBytes,
-            const bool       lastIteration)
-    {
-        /* In this case, the "zero-ing" is quite simple as the region
-         * to be zeroed sits in contiguous memory (row-major). */
-        const uint32_t eraseLen = strideSzBytes * this->m_outputContextLen;
-
-        /* Erase left context? */
-        if (this->m_countIterations > 0) {
-            /* Set output of each classification window to the blank token. */
-            std::memset(ptrData, 0, eraseLen);
-            for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) {
-                ptrData[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1;
-            }
-        }
-
-        /* Erase right context? */
-        if (false == lastIteration) {
-            uint8_t* rightCtxPtr = ptrData + (strideSzBytes * (this->m_outputContextLen + this->m_outputInnerLen));
-            /* Set output of each classification window to the blank token. */
-            std::memset(rightCtxPtr, 0, eraseLen);
-            for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) {
-                rightCtxPtr[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1;
-            }
-        }
-
-        if (lastIteration) {
-            this->m_countIterations = 0;
-        } else {
-            ++this->m_countIterations;
-        }
-
-        return true;
-    }
-
-    uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model)
-    {
-        TfLiteTensor* inputTensor = model.GetInputTensor(0);
-        const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0);
-        if (inputRows == 0) {
-            printf_err("Error getting number of input rows for axis: %" PRIu32 "\n",
-                    Wav2LetterModel::ms_inputRowsIdx);
-        }
-        return inputRows;
-    }
-
-    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)
-    {
-        const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model);
-        const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
-        constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx;
-
-        /* Check to make sure that the input tensor supports the above
-         * context and inner lengths. */
-        if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) {
-            printf_err("Input rows not compatible with ctx of %" PRIu32 "\n",
-                       inputCtxLen);
-            return 0;
-        }
-
-        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-        const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0);
-        if (outputRows == 0) {
-            printf_err("Error getting number of output rows for axis: %" PRIu32 "\n",
-                       Wav2LetterModel::ms_outputRowsIdx);
-            return 0;
-        }
-
-        const float inOutRowRatio = static_cast<float>(inputRows) /
-                                     static_cast<float>(outputRows);
-
-        return std::round(static_cast<float>(inputCtxLen) / inOutRowRatio);
-    }
-
-} /* namespace app */
-} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
deleted file mode 100644
index 92b0631..0000000
--- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
+++ /dev/null
@@ -1,208 +0,0 @@
-/*
- * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
- * SPDX-License-Identifier: Apache-2.0
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
-#include "Wav2LetterPreprocess.hpp"
-
-#include "PlatformMath.hpp"
-#include "TensorFlowLiteMicro.hpp"
-
-#include <algorithm>
-#include <cmath>
-
-namespace arm {
-namespace app {
-
-    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),
-            m_mfccBuf(numMfccFeatures, numFeatureFrames),
-            m_delta1Buf(numMfccFeatures, numFeatureFrames),
-            m_delta2Buf(numMfccFeatures, numFeatureFrames),
-            m_mfccWindowLen(mfccWindowLen),
-            m_mfccWindowStride(mfccWindowStride),
-            m_numMfccFeats(numMfccFeatures),
-            m_numFeatureFrames(numFeatureFrames)
-    {
-        if (numMfccFeatures > 0 && mfccWindowLen > 0) {
-            this->m_mfcc.Init();
-        }
-    }
-
-    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,
-                this->m_mfccWindowLen, this->m_mfccWindowStride);
-
-        uint32_t mfccBufIdx = 0;
-
-        std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f);
-        std::fill(m_delta1Buf.begin(), m_delta1Buf.end(), 0.f);
-        std::fill(m_delta2Buf.begin(), m_delta2Buf.end(), 0.f);
-
-        /* While we can slide over the audio. */
-        while (this->m_mfccSlidingWindow.HasNext()) {
-            const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
-            auto mfccAudioData = std::vector<int16_t>(
-                                        mfccWindow,
-                                        mfccWindow + this->m_mfccWindowLen);
-            auto mfcc = this->m_mfcc.MfccCompute(mfccAudioData);
-            for (size_t i = 0; i < this->m_mfccBuf.size(0); ++i) {
-                this->m_mfccBuf(i, mfccBufIdx) = mfcc[i];
-            }
-            ++mfccBufIdx;
-        }
-
-        /* Pad MFCC if needed by adding MFCC for zeros. */
-        if (mfccBufIdx != this->m_numFeatureFrames) {
-            std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_mfccWindowLen, 0);
-            std::vector<float> mfccZeros = this->m_mfcc.MfccCompute(zerosWindow);
-
-            while (mfccBufIdx != this->m_numFeatureFrames) {
-                memcpy(&this->m_mfccBuf(0, mfccBufIdx),
-                       mfccZeros.data(), sizeof(float) * m_numMfccFeats);
-                ++mfccBufIdx;
-            }
-        }
-
-        /* Compute first and second order deltas from MFCCs. */
-        AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
-
-        /* Standardize calculated features. */
-        this->Standarize();
-
-        /* Quantise. */
-        QuantParams quantParams = GetTensorQuantParams(this->m_inputTensor);
-
-        if (0 == quantParams.scale) {
-            printf_err("Quantisation scale can't be 0\n");
-            return false;
-        }
-
-        switch(this->m_inputTensor->type) {
-            case kTfLiteUInt8:
-                return this->Quantise<uint8_t>(
-                        tflite::GetTensorData<uint8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
-                        quantParams.scale, quantParams.offset);
-            case kTfLiteInt8:
-                return this->Quantise<int8_t>(
-                        tflite::GetTensorData<int8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
-                        quantParams.scale, quantParams.offset);
-            default:
-                printf_err("Unsupported tensor type %s\n",
-                    TfLiteTypeGetName(this->m_inputTensor->type));
-        }
-
-        return false;
-    }
-
-    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,
-             1.66666667e-02, -3.46944695e-18, -1.66666667e-02,
-            -3.33333333e-02, -5.00000000e-02, -6.66666667e-02};
-
-        const std::vector <float> delta2Coeffs =
-            {0.06060606,      0.01515152,     -0.01731602,
-            -0.03679654,     -0.04329004,     -0.03679654,
-            -0.01731602,      0.01515152,      0.06060606};
-
-        if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) ||
-            mfcc.size(0) == 0 || mfcc.size(1) == 0) {
-            return false;
-        }
-
-        /* Get the middle index; coeff vec len should always be odd. */
-        const size_t coeffLen = delta1Coeffs.size();
-        const size_t fMidIdx = (coeffLen - 1)/2;
-        const size_t numFeatures = mfcc.size(0);
-        const size_t numFeatVectors = mfcc.size(1);
-
-        /* Iterate through features in MFCC vector. */
-        for (size_t i = 0; i < numFeatures; ++i) {
-            /* For each feature, iterate through time (t) samples representing feature evolution and
-             * calculate d/dt and d^2/dt^2, using 1D convolution with differential kernels.
-             * Convolution padding = valid, result size is `time length - kernel length + 1`.
-             * The result is padded with 0 from both sides to match the size of initial time samples data.
-             *
-             * For the small filter, conv1D implementation as a simple loop is efficient enough.
-             * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32.
-             */
-
-            for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) {
-                float d1 = 0;
-                float d2 = 0;
-                const size_t mfccStIdx = j - fMidIdx;
-
-                for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) {
-
-                    d1 +=  mfcc(i,mfccStIdx + k) * delta1Coeffs[m];
-                    d2 +=  mfcc(i,mfccStIdx + k) * delta2Coeffs[m];
-                }
-
-                delta1(i,j) = d1;
-                delta2(i,j) = d2;
-            }
-        }
-
-        return true;
-    }
-
-    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);
-
-        debug("Mean: %f, Stddev: %f\n", mean, stddev);
-        if (stddev == 0) {
-            std::fill(vec.begin(), vec.end(), 0);
-        } else {
-            const float stddevInv = 1.f/stddev;
-            const float normalisedMean = mean/stddev;
-
-            auto NormalisingFunction = [=](float& value) {
-                value = value * stddevInv - normalisedMean;
-            };
-            std::for_each(vec.begin(), vec.end(), NormalisingFunction);
-        }
-    }
-
-    void AsrPreProcess::Standarize()
-    {
-        AsrPreProcess::StandardizeVecF32(this->m_mfccBuf);
-        AsrPreProcess::StandardizeVecF32(this->m_delta1Buf);
-        AsrPreProcess::StandardizeVecF32(this->m_delta2Buf);
-    }
-
-    float AsrPreProcess::GetQuantElem(
-                const float     elem,
-                const float     quantScale,
-                const int       quantOffset,
-                const float     minVal,
-                const float     maxVal)
-    {
-        float val = std::round((elem/quantScale) + quantOffset);
-        return std::min<float>(std::max<float>(val, minVal), maxVal);
-    }
-
-} /* namespace app */
-} /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/kws_asr/usecase.cmake b/source/use_case/kws_asr/usecase.cmake
index 40df4d7..59ef450 100644
--- a/source/use_case/kws_asr/usecase.cmake
+++ b/source/use_case/kws_asr/usecase.cmake
@@ -14,6 +14,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 #----------------------------------------------------------------------------
+# Append the APIs to use for this use case
+list(APPEND ${use_case}_API_LIST "kws" "asr")
 
 USER_OPTION(${use_case}_FILE_PATH "Directory with WAV files, or path to a single WAV file, to use in the evaluation application."
     ${CMAKE_CURRENT_SOURCE_DIR}/resources/${use_case}/samples/
@@ -145,4 +147,4 @@
         ${${use_case}_AUDIO_OFFSET}
         ${${use_case}_AUDIO_DURATION}
         ${${use_case}_AUDIO_RES_TYPE}
-        ${${use_case}_AUDIO_MIN_SAMPLES})
\ No newline at end of file
+        ${${use_case}_AUDIO_MIN_SAMPLES})