MLECO-3183: Refactoring application sources

Platform agnostic application sources are moved into application
api module with their own independent CMake projects.

Changes for MLECO-3080 also included - they create CMake projects
individial API's (again, platform agnostic) that dependent on the
common logic. The API for KWS_API "joint" API has been removed and
now the use case relies on individual KWS, and ASR API libraries.

Change-Id: I1f7748dc767abb3904634a04e0991b74ac7b756d
Signed-off-by: Kshitij Sisodia <kshitij.sisodia@arm.com>

7 files changed, 605 insertions, 0 deletions

diff --git a/source/application/api/use_case/kws/CMakeLists.txt b/source/application/api/use_case/kws/CMakeLists.txt
new file mode 100644
index 0000000..3256d03
--- /dev/null
+++ b/source/application/api/use_case/kws/CMakeLists.txt
@@ -0,0 +1,39 @@
+#----------------------------------------------------------------------------
+#  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.
+#----------------------------------------------------------------------------
+#########################################################
+#            KEYWORD SPOTTING API library               #
+#########################################################
+cmake_minimum_required(VERSION 3.15.6)
+
+set(KWS_API_TARGET kws_api)
+project(${KWS_API_TARGET}
+        DESCRIPTION     "Keyword spotting use case API library"
+        LANGUAGES       C CXX)
+
+# Create static library
+add_library(${KWS_API_TARGET} STATIC
+    src/KwsProcessing.cc
+    src/MicroNetKwsModel.cc)
+
+target_include_directories(${KWS_API_TARGET} PUBLIC include)
+
+target_link_libraries(${KWS_API_TARGET} PUBLIC common_api)
+
+message(STATUS "*******************************************************")
+message(STATUS "Library                                : " ${KWS_API_TARGET})
+message(STATUS "CMAKE_SYSTEM_PROCESSOR                 : " ${CMAKE_SYSTEM_PROCESSOR})
+message(STATUS "*******************************************************")
diff --git a/source/application/api/use_case/kws/include/KwsProcessing.hpp b/source/application/api/use_case/kws/include/KwsProcessing.hpp
new file mode 100644
index 0000000..0ede425
--- /dev/null
+++ b/source/application/api/use_case/kws/include/KwsProcessing.hpp
@@ -0,0 +1,137 @@
+/*
+ * 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 "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/application/api/use_case/kws/include/KwsResult.hpp b/source/application/api/use_case/kws/include/KwsResult.hpp
new file mode 100644
index 0000000..38f32b4
--- /dev/null
+++ b/source/application/api/use_case/kws/include/KwsResult.hpp
@@ -0,0 +1,63 @@
+/*
+ * 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/application/api/use_case/kws/include/MicroNetKwsMfcc.hpp b/source/application/api/use_case/kws/include/MicroNetKwsMfcc.hpp
new file mode 100644
index 0000000..b2565a3
--- /dev/null
+++ b/source/application/api/use_case/kws/include/MicroNetKwsMfcc.hpp
@@ -0,0 +1,50 @@
+/*
+ * 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_MICRONET_MFCC_HPP
+#define KWS_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_MICRONET_MFCC_HPP */
\ No newline at end of file
diff --git a/source/application/api/use_case/kws/include/MicroNetKwsModel.hpp b/source/application/api/use_case/kws/include/MicroNetKwsModel.hpp
new file mode 100644
index 0000000..3d2f3de
--- /dev/null
+++ b/source/application/api/use_case/kws/include/MicroNetKwsModel.hpp
@@ -0,0 +1,63 @@
+/*
+ * 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_MICRONETMODEL_HPP
+#define KWS_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;
+
+    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_MICRONETMODEL_HPP */
diff --git a/source/application/api/use_case/kws/src/KwsProcessing.cc b/source/application/api/use_case/kws/src/KwsProcessing.cc
new file mode 100644
index 0000000..40de498
--- /dev/null
+++ b/source/application/api/use_case/kws/src/KwsProcessing.cc
@@ -0,0 +1,211 @@
+/*
+ * 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 "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/application/api/use_case/kws/src/MicroNetKwsModel.cc b/source/application/api/use_case/kws/src/MicroNetKwsModel.cc
new file mode 100644
index 0000000..bedca99
--- /dev/null
+++ b/source/application/api/use_case/kws/src/MicroNetKwsModel.cc
@@ -0,0 +1,42 @@
+/*
+ * 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"
+
+const tflite::MicroOpResolver& arm::app::MicroNetKwsModel::GetOpResolver()
+{
+    return this->m_opResolver;
+}
+
+bool arm::app::MicroNetKwsModel::EnlistOperations()
+{
+    this->m_opResolver.AddReshape();
+    this->m_opResolver.AddAveragePool2D();
+    this->m_opResolver.AddConv2D();
+    this->m_opResolver.AddDepthwiseConv2D();
+    this->m_opResolver.AddFullyConnected();
+    this->m_opResolver.AddRelu();
+
+    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;
+    }
+    return true;
+}