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>
diff --git a/source/application/api/use_case/ad/CMakeLists.txt b/source/application/api/use_case/ad/CMakeLists.txt
new file mode 100644
index 0000000..224816f
--- /dev/null
+++ b/source/application/api/use_case/ad/CMakeLists.txt
@@ -0,0 +1,41 @@
+#----------------------------------------------------------------------------
+# 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.
+#----------------------------------------------------------------------------
+#########################################################
+# ANOMALY DETECTION API library #
+#########################################################
+cmake_minimum_required(VERSION 3.15.6)
+
+set(AD_API_TARGET ad_api)
+project(${AD_API_TARGET}
+ DESCRIPTION "Anomaly detection use case API library"
+ LANGUAGES C CXX)
+
+# Create static library
+add_library(${AD_API_TARGET} STATIC
+ src/AdModel.cc
+ src/AdProcessing.cc
+ src/AdMelSpectrogram.cc
+ src/MelSpectrogram.cc)
+
+target_include_directories(${AD_API_TARGET} PUBLIC include)
+
+target_link_libraries(${AD_API_TARGET} PUBLIC common_api)
+
+message(STATUS "*******************************************************")
+message(STATUS "Library : " ${AD_API_TARGET})
+message(STATUS "CMAKE_SYSTEM_PROCESSOR : " ${CMAKE_SYSTEM_PROCESSOR})
+message(STATUS "*******************************************************")
diff --git a/source/application/api/use_case/ad/include/AdMelSpectrogram.hpp b/source/application/api/use_case/ad/include/AdMelSpectrogram.hpp
new file mode 100644
index 0000000..05c5bfc
--- /dev/null
+++ b/source/application/api/use_case/ad/include/AdMelSpectrogram.hpp
@@ -0,0 +1,97 @@
+/*
+ * 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 ADMELSPECTROGRAM_HPP
+#define ADMELSPECTROGRAM_HPP
+
+#include "MelSpectrogram.hpp"
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ /* Class to provide anomaly detection specific Mel Spectrogram calculation requirements */
+ class AdMelSpectrogram : public MelSpectrogram {
+
+ public:
+ static constexpr uint32_t ms_defaultSamplingFreq = 16000;
+ static constexpr uint32_t ms_defaultNumFbankBins = 64;
+ static constexpr uint32_t ms_defaultMelLoFreq = 0;
+ static constexpr uint32_t ms_defaultMelHiFreq = 8000;
+ static constexpr bool ms_defaultUseHtkMethod = false;
+
+ explicit AdMelSpectrogram(const size_t frameLen)
+ : MelSpectrogram(MelSpecParams(
+ ms_defaultSamplingFreq, ms_defaultNumFbankBins,
+ ms_defaultMelLoFreq, ms_defaultMelHiFreq,
+ frameLen, ms_defaultUseHtkMethod))
+ {}
+
+ AdMelSpectrogram() = delete;
+ ~AdMelSpectrogram() = 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
+ */
+ virtual 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
+ **/
+ virtual void ConvertToLogarithmicScale(std::vector<float>& melEnergies) 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 Return float value to be applied
+ * when populating the filter bank.
+ */
+ virtual float GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ const bool useHTKMethod) override;
+ };
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* ADMELSPECTROGRAM_HPP */
diff --git a/source/application/api/use_case/ad/include/AdModel.hpp b/source/application/api/use_case/ad/include/AdModel.hpp
new file mode 100644
index 0000000..0436a89
--- /dev/null
+++ b/source/application/api/use_case/ad/include/AdModel.hpp
@@ -0,0 +1,55 @@
+/*
+ * 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 AD_MODEL_HPP
+#define AD_MODEL_HPP
+
+#include "Model.hpp"
+
+extern const int g_FrameLength;
+extern const int g_FrameStride;
+extern const float g_ScoreThreshold;
+extern const float g_TrainingMean;
+
+namespace arm {
+namespace app {
+
+ class AdModel : public Model {
+
+ public:
+ /* Indices for the expected model - based on input tensor shape */
+ static constexpr uint32_t ms_inputRowsIdx = 1;
+ static constexpr uint32_t ms_inputColsIdx = 2;
+
+ 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 = 6;
+
+ /* A mutable op resolver instance */
+ tflite::MicroMutableOpResolver<ms_maxOpCnt> m_opResolver;
+ };
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* AD_MODEL_HPP */
diff --git a/source/application/api/use_case/ad/include/AdProcessing.hpp b/source/application/api/use_case/ad/include/AdProcessing.hpp
new file mode 100644
index 0000000..abee75e
--- /dev/null
+++ b/source/application/api/use_case/ad/include/AdProcessing.hpp
@@ -0,0 +1,231 @@
+/*
+ * 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 AD_PROCESSING_HPP
+#define AD_PROCESSING_HPP
+
+#include "BaseProcessing.hpp"
+#include "TensorFlowLiteMicro.hpp"
+#include "AudioUtils.hpp"
+#include "AdMelSpectrogram.hpp"
+#include "log_macros.h"
+
+namespace arm {
+namespace app {
+
+ /**
+ * @brief Pre-processing class for anomaly detection use case.
+ * Implements methods declared by BasePreProcess and anything else needed
+ * to populate input tensors ready for inference.
+ */
+ class AdPreProcess : public BasePreProcess {
+
+ public:
+ /**
+ * @brief Constructor for AdPreProcess class objects
+ * @param[in] inputTensor input tensor pointer from the tensor arena.
+ * @param[in] melSpectrogramFrameLen MEL spectrogram's frame length
+ * @param[in] melSpectrogramFrameStride MEL spectrogram's frame stride
+ * @param[in] adModelTrainingMean Training mean for the Anomaly detection model being used.
+ */
+ explicit AdPreProcess(TfLiteTensor* inputTensor,
+ uint32_t melSpectrogramFrameLen,
+ uint32_t melSpectrogramFrameStride,
+ float adModelTrainingMean);
+
+ ~AdPreProcess() = default;
+
+ /**
+ * @brief Function to invoke pre-processing and populate the input vector
+ * @param input pointer to input data. For anomaly detection, this is the pointer to
+ * the audio data.
+ * @param inputSize Size of the data being passed in for pre-processing.
+ * @return True if successful, false otherwise.
+ */
+ bool DoPreProcess(const void* input, size_t inputSize) override;
+
+ /**
+ * @brief Getter function for audio window size computed when constructing
+ * the class object.
+ * @return Audio window size as 32 bit unsigned integer.
+ */
+ uint32_t GetAudioWindowSize();
+
+ /**
+ * @brief Getter function for audio window stride computed when constructing
+ * the class object.
+ * @return Audio window stride as 32 bit unsigned integer.
+ */
+ uint32_t GetAudioDataStride();
+
+ /**
+ * @brief Setter function for current audio index. This is only used for evaluating
+ * if previously computed features can be re-used from cache.
+ */
+ void SetAudioWindowIndex(uint32_t idx);
+
+ private:
+ bool m_validInstance{false}; /**< Indicates the current object is valid. */
+ uint32_t m_melSpectrogramFrameLen{}; /**< MEL spectrogram's window frame length */
+ uint32_t m_melSpectrogramFrameStride{}; /**< MEL spectrogram's window frame stride */
+ uint8_t m_inputResizeScale{}; /**< Downscaling factor for the MEL energy matrix. */
+ uint32_t m_numMelSpecVectorsInAudioStride{}; /**< Number of frames to move across the audio. */
+ uint32_t m_audioDataWindowSize{}; /**< Audio window size computed based on other parameters. */
+ uint32_t m_audioDataStride{}; /**< Audio window stride computed. */
+ uint32_t m_numReusedFeatureVectors{}; /**< Number of MEL vectors that can be re-used */
+ uint32_t m_audioWindowIndex{}; /**< Current audio window index (from audio's sliding window) */
+
+ audio::SlidingWindow<const int16_t> m_melWindowSlider; /**< Internal MEL spectrogram window slider */
+ audio::AdMelSpectrogram m_melSpec; /**< MEL spectrogram computation object */
+ std::function<void
+ (std::vector<int16_t>&, int, bool, size_t, size_t)> m_featureCalc; /**< Feature calculator object */
+ };
+
+ class AdPostProcess : public BasePostProcess {
+ public:
+ /**
+ * @brief Constructor for AdPostProcess object.
+ * @param[in] outputTensor Output tensor pointer.
+ */
+ explicit AdPostProcess(TfLiteTensor* outputTensor);
+
+ ~AdPostProcess() = default;
+
+ /**
+ * @brief Function to do the post-processing on the output tensor.
+ * @return True if successful, false otherwise.
+ */
+ bool DoPostProcess() override;
+
+ /**
+ * @brief Getter function for an element from the de-quantised output vector.
+ * @param index Index of the element to be retrieved.
+ * @return index represented as a 32 bit floating point number.
+ */
+ float GetOutputValue(uint32_t index);
+
+ private:
+ TfLiteTensor* m_outputTensor{}; /**< Output tensor pointer */
+ std::vector<float> m_dequantizedOutputVec{}; /**< Internal output vector */
+
+ /**
+ * @brief De-quantizes and flattens the output tensor into a vector.
+ * @tparam T template parameter to indicate data type.
+ * @return True if successful, false otherwise.
+ */
+ template<typename T>
+ bool Dequantize()
+ {
+ TfLiteTensor* tensor = this->m_outputTensor;
+ if (tensor == nullptr) {
+ printf_err("Invalid output tensor.\n");
+ return false;
+ }
+ T* tensorData = tflite::GetTensorData<T>(tensor);
+
+ uint32_t totalOutputSize = 1;
+ for (int inputDim = 0; inputDim < tensor->dims->size; inputDim++){
+ totalOutputSize *= tensor->dims->data[inputDim];
+ }
+
+ /* For getting the floating point values, we need quantization parameters */
+ QuantParams quantParams = GetTensorQuantParams(tensor);
+
+ this->m_dequantizedOutputVec = std::vector<float>(totalOutputSize, 0);
+
+ for (size_t i = 0; i < totalOutputSize; ++i) {
+ this->m_dequantizedOutputVec[i] = quantParams.scale * (tensorData[i] - quantParams.offset);
+ }
+
+ return true;
+ }
+ };
+
+ /* Templated instances available: */
+ template bool AdPostProcess::Dequantize<int8_t>();
+
+ /**
+ * @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 inputTensor model input tensor pointer.
+ * @param cacheSize number of feature vectors to cache. Defined by the sliding window overlap.
+ * @param 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, size_t)>
+ FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+ std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+ {
+ /* Feature cache to be captured by lambda function*/
+ static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
+
+ return [=](std::vector<int16_t>& audioDataWindow,
+ size_t index,
+ bool useCache,
+ size_t featuresOverlapIndex,
+ size_t resizeScale)
+ {
+ 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() / resizeScale;
+ auto sizeBytes = sizeof(T);
+
+ /* Input should be transposed and "resized" by skipping elements. */
+ for (size_t outIndex = 0; outIndex < size; outIndex++) {
+ std::memcpy(tensorData + (outIndex*size) + index, &features[outIndex*resizeScale], sizeBytes);
+ }
+
+ /* Start renewing cache as soon iteration goes out of the windows overlap. */
+ if (index >= featuresOverlapIndex / resizeScale) {
+ featureCache[index - featuresOverlapIndex / resizeScale] = std::move(features);
+ }
+ };
+ }
+
+ template std::function<void (std::vector<int16_t>&, size_t , bool, size_t, size_t)>
+ FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ std::function<std::vector<int8_t> (std::vector<int16_t>&)> compute);
+
+ template std::function<void(std::vector<int16_t>&, size_t, bool, size_t, size_t)>
+ 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, size_t)>
+ GetFeatureCalculator(audio::AdMelSpectrogram& melSpec,
+ TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ float trainingMean);
+
+} /* namespace app */
+} /* namespace arm */
+
+#endif /* AD_PROCESSING_HPP */
diff --git a/source/application/api/use_case/ad/include/MelSpectrogram.hpp b/source/application/api/use_case/ad/include/MelSpectrogram.hpp
new file mode 100644
index 0000000..d3ea3f7
--- /dev/null
+++ b/source/application/api/use_case/ad/include/MelSpectrogram.hpp
@@ -0,0 +1,234 @@
+/*
+ * 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 MELSPECTROGRAM_HPP
+#define MELSPECTROGRAM_HPP
+
+#include "PlatformMath.hpp"
+
+#include <vector>
+#include <cstdint>
+#include <cmath>
+#include <limits>
+#include <string>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ /* Mel Spectrogram consolidated parameters */
+ class MelSpecParams {
+ public:
+ float m_samplingFreq;
+ uint32_t m_numFbankBins;
+ float m_melLoFreq;
+ float m_melHiFreq;
+ uint32_t m_frameLen;
+ uint32_t m_frameLenPadded;
+ bool m_useHtkMethod;
+
+ /** @brief Constructor */
+ MelSpecParams(const float samplingFreq, const uint32_t numFbankBins,
+ const float melLoFreq, const float melHiFreq,
+ const uint32_t frameLen, const bool useHtkMethod);
+
+ MelSpecParams() = delete;
+ ~MelSpecParams() = default;
+
+ /** @brief String representation of parameters */
+ std::string Str() const;
+ };
+
+ /**
+ * @brief Class for Mel Spectrogram feature extraction.
+ * Based on https://github.com/ARM-software/ML-KWS-for-MCU/blob/master/Deployment/Source/MFCC/mfcc.cpp
+ * This class is designed to be generic and self-sufficient but
+ * certain calculation routines can be overridden to accommodate
+ * use-case specific requirements.
+ */
+ class MelSpectrogram {
+
+ public:
+ /**
+ * @brief Extract Mel Spectrogram for one single small frame of
+ * audio data e.g. 640 samples.
+ * @param[in] audioData Vector of audio samples to calculate
+ * features for.
+ * @param[in] trainingMean Value to subtract from the the computed mel spectrogram, default 0.
+ * @return Vector of extracted Mel Spectrogram features.
+ **/
+ std::vector<float> ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean = 0);
+
+ /**
+ * @brief Constructor
+ * @param[in] params Mel Spectrogram parameters
+ */
+ explicit MelSpectrogram(const MelSpecParams& params);
+
+ MelSpectrogram() = delete;
+ ~MelSpectrogram() = default;
+
+ /** @brief Initialise */
+ void Init();
+
+ /**
+ * @brief Extract Mel Spectrogram features and quantise for one single small
+ * frame of audio data e.g. 640 samples.
+ * @param[in] audioData Vector of audio samples to calculate
+ * features for.
+ * @param[in] quantScale quantisation scale.
+ * @param[in] quantOffset quantisation offset.
+ * @param[in] trainingMean training mean.
+ * @return Vector of extracted quantised Mel Spectrogram features.
+ **/
+ template<typename T>
+ std::vector<T> MelSpecComputeQuant(const std::vector<int16_t>& audioData,
+ const float quantScale,
+ const int quantOffset,
+ float trainingMean = 0)
+ {
+ this->ComputeMelSpec(audioData, trainingMean);
+ float minVal = std::numeric_limits<T>::min();
+ float maxVal = std::numeric_limits<T>::max();
+
+ std::vector<T> melSpecOut(this->m_params.m_numFbankBins);
+ const size_t numFbankBins = this->m_params.m_numFbankBins;
+
+ /* Quantize to T. */
+ for (size_t k = 0; k < numFbankBins; ++k) {
+ auto quantizedEnergy = std::round(((this->m_melEnergies[k]) / quantScale) + quantOffset);
+ melSpecOut[k] = static_cast<T>(std::min<float>(std::max<float>(quantizedEnergy, minVal), maxVal));
+ }
+
+ return melSpecOut;
+ }
+
+ /* Constants */
+ static constexpr float ms_logStep = /*logf(6.4)*/ 1.8562979903656 / 27.0;
+ static constexpr float ms_freqStep = 200.0 / 3;
+ static constexpr float ms_minLogHz = 1000.0;
+ static constexpr float ms_minLogMel = ms_minLogHz / ms_freqStep;
+
+ protected:
+ /**
+ * @brief Project input frequency to Mel Scale.
+ * @param[in] freq input frequency in floating point
+ * @param[in] useHTKMethod bool to signal if HTK method is to be
+ * used for calculation
+ * @return Mel transformed frequency in floating point
+ **/
+ static float MelScale(const float freq,
+ const bool useHTKMethod = true);
+
+ /**
+ * @brief Inverse Mel transform - convert MEL warped frequency
+ * back to normal frequency
+ * @param[in] melFreq Mel frequency in floating point
+ * @param[in] useHTKMethod bool to signal if HTK method is to be
+ * used for calculation
+ * @return Real world frequency in floating point
+ **/
+ static float InverseMelScale(const float melFreq,
+ const bool useHTKMethod = true);
+
+ /**
+ * @brief Populates MEL energies after applying the MEL filter
+ * bank weights and adding them up to be placed into
+ * bins, according to the filter bank's first and last
+ * indices (pre-computed for each filter bank element
+ * by CreateMelFilterBank 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
+ */
+ virtual 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);
+
+ /**
+ * @brief Converts the Mel energies for logarithmic scale
+ * @param[in,out] melEnergies 1D vector of Mel energies
+ **/
+ virtual void ConvertToLogarithmicScale(std::vector<float>& melEnergies);
+
+ /**
+ * @brief Given the low and high Mel values, get the normaliser
+ * for weights to be applied when populating the filter
+ * bank.
+ * @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 Return float value to be applied
+ * when populating the filter bank.
+ */
+ virtual float GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ const bool useHTKMethod);
+
+ private:
+ MelSpecParams m_params;
+ std::vector<float> m_frame;
+ std::vector<float> m_buffer;
+ std::vector<float> m_melEnergies;
+ std::vector<float> m_windowFunc;
+ std::vector<std::vector<float>> m_melFilterBank;
+ std::vector<uint32_t> m_filterBankFilterFirst;
+ std::vector<uint32_t> m_filterBankFilterLast;
+ bool m_filterBankInitialised;
+ arm::app::math::FftInstance m_fftInstance;
+
+ /**
+ * @brief Initialises the filter banks.
+ **/
+ void InitMelFilterBank();
+
+ /**
+ * @brief Signals whether the instance of MelSpectrogram has had its
+ * required buffers initialised
+ * @return True if initialised, false otherwise
+ **/
+ bool IsMelFilterBankInited() const;
+
+ /**
+ * @brief Create mel filter banks for Mel Spectrogram calculation.
+ * @return 2D vector of floats
+ **/
+ std::vector<std::vector<float>> CreateMelFilterBank();
+
+ /**
+ * @brief Computes the magnitude from an interleaved complex array
+ **/
+ void ConvertToPowerSpectrum();
+
+ };
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
+
+
+#endif /* MELSPECTROGRAM_HPP */
diff --git a/source/application/api/use_case/ad/src/AdMelSpectrogram.cc b/source/application/api/use_case/ad/src/AdMelSpectrogram.cc
new file mode 100644
index 0000000..14b9323
--- /dev/null
+++ b/source/application/api/use_case/ad/src/AdMelSpectrogram.cc
@@ -0,0 +1,93 @@
+/*
+ * 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 "AdMelSpectrogram.hpp"
+#include "PlatformMath.hpp"
+#include "log_macros.h"
+
+#include <cfloat>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ bool AdMelSpectrogram::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();
+ float melEnergy = FLT_MIN; /* Avoid log of zero at later stages. */
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<int32_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 AdMelSpectrogram::ConvertToLogarithmicScale(
+ std::vector<float>& melEnergies)
+ {
+ /* 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. */
+ for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
+ iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
+
+ *iterM = *iterL * multiplier;
+ }
+ }
+
+ float AdMelSpectrogram::GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ const bool useHTKMethod)
+ {
+ /* Slaney normalization for mel weights. */
+ return (2.0f / (AdMelSpectrogram::InverseMelScale(rightMel, useHTKMethod) -
+ AdMelSpectrogram::InverseMelScale(leftMel, useHTKMethod)));
+ }
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */
diff --git a/source/application/api/use_case/ad/src/AdModel.cc b/source/application/api/use_case/ad/src/AdModel.cc
new file mode 100644
index 0000000..961c260
--- /dev/null
+++ b/source/application/api/use_case/ad/src/AdModel.cc
@@ -0,0 +1,41 @@
+/*
+ * 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 "AdModel.hpp"
+#include "log_macros.h"
+
+const tflite::MicroOpResolver& arm::app::AdModel::GetOpResolver()
+{
+ return this->m_opResolver;
+}
+
+bool arm::app::AdModel::EnlistOperations()
+{
+ this->m_opResolver.AddAveragePool2D();
+ this->m_opResolver.AddConv2D();
+ this->m_opResolver.AddDepthwiseConv2D();
+ this->m_opResolver.AddRelu6();
+ this->m_opResolver.AddReshape();
+
+ 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;
+}
diff --git a/source/application/api/use_case/ad/src/AdProcessing.cc b/source/application/api/use_case/ad/src/AdProcessing.cc
new file mode 100644
index 0000000..fb26a83
--- /dev/null
+++ b/source/application/api/use_case/ad/src/AdProcessing.cc
@@ -0,0 +1,210 @@
+/*
+ * 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 "AdProcessing.hpp"
+
+#include "AdModel.hpp"
+
+namespace arm {
+namespace app {
+
+AdPreProcess::AdPreProcess(TfLiteTensor* inputTensor,
+ uint32_t melSpectrogramFrameLen,
+ uint32_t melSpectrogramFrameStride,
+ float adModelTrainingMean):
+ m_validInstance{false},
+ m_melSpectrogramFrameLen{melSpectrogramFrameLen},
+ m_melSpectrogramFrameStride{melSpectrogramFrameStride},
+ /**< Model is trained on features downsampled 2x */
+ m_inputResizeScale{2},
+ /**< We are choosing to move by 20 frames across the audio for each inference. */
+ m_numMelSpecVectorsInAudioStride{20},
+ m_audioDataStride{m_numMelSpecVectorsInAudioStride * melSpectrogramFrameStride},
+ m_melSpec{melSpectrogramFrameLen}
+{
+ UNUSED(this->m_melSpectrogramFrameStride);
+
+ if (!inputTensor) {
+ printf_err("Invalid input tensor provided to pre-process\n");
+ return;
+ }
+
+ TfLiteIntArray* inputShape = inputTensor->dims;
+
+ if (!inputShape) {
+ printf_err("Invalid input tensor dims\n");
+ return;
+ }
+
+ const uint32_t kNumRows = inputShape->data[AdModel::ms_inputRowsIdx];
+ const uint32_t kNumCols = inputShape->data[AdModel::ms_inputColsIdx];
+
+ /* Deduce the data length required for 1 inference from the network parameters. */
+ this->m_audioDataWindowSize = (((this->m_inputResizeScale * kNumCols) - 1) *
+ melSpectrogramFrameStride) +
+ melSpectrogramFrameLen;
+ this->m_numReusedFeatureVectors = kNumRows -
+ (this->m_numMelSpecVectorsInAudioStride /
+ this->m_inputResizeScale);
+ this->m_melSpec.Init();
+
+ /* Creating a Mel Spectrogram sliding window for the data required for 1 inference.
+ * "resizing" done here by multiplying stride by resize scale. */
+ this->m_melWindowSlider = audio::SlidingWindow<const int16_t>(
+ nullptr, /* to be populated later. */
+ this->m_audioDataWindowSize,
+ melSpectrogramFrameLen,
+ melSpectrogramFrameStride * this->m_inputResizeScale);
+
+ /* Construct feature calculation function. */
+ this->m_featureCalc = GetFeatureCalculator(this->m_melSpec, inputTensor,
+ this->m_numReusedFeatureVectors,
+ adModelTrainingMean);
+ this->m_validInstance = true;
+}
+
+bool AdPreProcess::DoPreProcess(const void* input, size_t inputSize)
+{
+ /* Check that we have a valid instance. */
+ if (!this->m_validInstance) {
+ printf_err("Invalid pre-processor instance\n");
+ return false;
+ }
+
+ /* We expect that we can traverse the size with which the MEL spectrogram
+ * sliding window was initialised with. */
+ if (!input || inputSize < this->m_audioDataWindowSize) {
+ printf_err("Invalid input provided for pre-processing\n");
+ return false;
+ }
+
+ /* We moved to the next window - set the features sliding to the new address. */
+ this->m_melWindowSlider.Reset(static_cast<const int16_t*>(input));
+
+ /* The first window does not have cache ready. */
+ const bool useCache = this->m_audioWindowIndex > 0 && this->m_numReusedFeatureVectors > 0;
+
+ /* Start calculating features inside one audio sliding window. */
+ while (this->m_melWindowSlider.HasNext()) {
+ const int16_t* melSpecWindow = this->m_melWindowSlider.Next();
+ std::vector<int16_t> melSpecAudioData = std::vector<int16_t>(
+ melSpecWindow,
+ melSpecWindow + this->m_melSpectrogramFrameLen);
+
+ /* Compute features for this window and write them to input tensor. */
+ this->m_featureCalc(melSpecAudioData,
+ this->m_melWindowSlider.Index(),
+ useCache,
+ this->m_numMelSpecVectorsInAudioStride,
+ this->m_inputResizeScale);
+ }
+
+ return true;
+}
+
+uint32_t AdPreProcess::GetAudioWindowSize()
+{
+ return this->m_audioDataWindowSize;
+}
+
+uint32_t AdPreProcess::GetAudioDataStride()
+{
+ return this->m_audioDataStride;
+}
+
+void AdPreProcess::SetAudioWindowIndex(uint32_t idx)
+{
+ this->m_audioWindowIndex = idx;
+}
+
+AdPostProcess::AdPostProcess(TfLiteTensor* outputTensor) :
+ m_outputTensor {outputTensor}
+{}
+
+bool AdPostProcess::DoPostProcess()
+{
+ switch (this->m_outputTensor->type) {
+ case kTfLiteInt8:
+ this->Dequantize<int8_t>();
+ break;
+ default:
+ printf_err("Unsupported tensor type");
+ return false;
+ }
+
+ math::MathUtils::SoftmaxF32(this->m_dequantizedOutputVec);
+ return true;
+}
+
+float AdPostProcess::GetOutputValue(uint32_t index)
+{
+ if (index < this->m_dequantizedOutputVec.size()) {
+ return this->m_dequantizedOutputVec[index];
+ }
+ printf_err("Invalid index for output\n");
+ return 0.0;
+}
+
+std::function<void (std::vector<int16_t>&, int, bool, size_t, size_t)>
+GetFeatureCalculator(audio::AdMelSpectrogram& melSpec,
+ TfLiteTensor* inputTensor,
+ size_t cacheSize,
+ float trainingMean)
+{
+ std::function<void (std::vector<int16_t>&, size_t, bool, size_t, size_t)> melSpecFeatureCalc;
+
+ TfLiteQuantization quant = inputTensor->quantization;
+
+ if (kTfLiteAffineQuantization == quant.type) {
+
+ auto* quantParams = static_cast<TfLiteAffineQuantization*>(quant.params);
+ const float quantScale = quantParams->scale->data[0];
+ const int quantOffset = quantParams->zero_point->data[0];
+
+ switch (inputTensor->type) {
+ case kTfLiteInt8: {
+ melSpecFeatureCalc = FeatureCalc<int8_t>(
+ inputTensor,
+ cacheSize,
+ [=, &melSpec](std::vector<int16_t>& audioDataWindow) {
+ return melSpec.MelSpecComputeQuant<int8_t>(
+ audioDataWindow,
+ quantScale,
+ quantOffset,
+ trainingMean);
+ }
+ );
+ break;
+ }
+ default:
+ printf_err("Tensor type %s not supported\n", TfLiteTypeGetName(inputTensor->type));
+ }
+ } else {
+ melSpecFeatureCalc = FeatureCalc<float>(
+ inputTensor,
+ cacheSize,
+ [=, &melSpec](
+ std::vector<int16_t>& audioDataWindow) {
+ return melSpec.ComputeMelSpec(
+ audioDataWindow,
+ trainingMean);
+ });
+ }
+ return melSpecFeatureCalc;
+}
+
+} /* namespace app */
+} /* namespace arm */
diff --git a/source/application/api/use_case/ad/src/MelSpectrogram.cc b/source/application/api/use_case/ad/src/MelSpectrogram.cc
new file mode 100644
index 0000000..ff0c536
--- /dev/null
+++ b/source/application/api/use_case/ad/src/MelSpectrogram.cc
@@ -0,0 +1,316 @@
+/*
+ * 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 "MelSpectrogram.hpp"
+
+#include "PlatformMath.hpp"
+#include "log_macros.h"
+
+#include <cfloat>
+#include <cinttypes>
+
+namespace arm {
+namespace app {
+namespace audio {
+
+ MelSpecParams::MelSpecParams(
+ const float samplingFreq,
+ const uint32_t numFbankBins,
+ const float melLoFreq,
+ const float melHiFreq,
+ const uint32_t frameLen,
+ const bool useHtkMethod):
+ m_samplingFreq(samplingFreq),
+ m_numFbankBins(numFbankBins),
+ m_melLoFreq(melLoFreq),
+ m_melHiFreq(melHiFreq),
+ m_frameLen(frameLen),
+
+ /* Smallest power of 2 >= frame length. */
+ m_frameLenPadded(pow(2, ceil((log(frameLen)/log(2))))),
+ m_useHtkMethod(useHtkMethod)
+ {}
+
+ std::string MelSpecParams::Str() const
+ {
+ char strC[1024];
+ snprintf(strC, sizeof(strC) - 1, "\n \
+ \n\t Sampling frequency: %f\
+ \n\t Number of filter banks: %" PRIu32 "\
+ \n\t Mel frequency limit (low): %f\
+ \n\t Mel frequency limit (high): %f\
+ \n\t Frame length: %" PRIu32 "\
+ \n\t Padded frame length: %" PRIu32 "\
+ \n\t Using HTK for Mel scale: %s\n",
+ this->m_samplingFreq, this->m_numFbankBins, this->m_melLoFreq,
+ this->m_melHiFreq, this->m_frameLen,
+ this->m_frameLenPadded, this->m_useHtkMethod ? "yes" : "no");
+ return std::string{strC};
+ }
+
+ MelSpectrogram::MelSpectrogram(const MelSpecParams& params):
+ m_params(params),
+ m_filterBankInitialised(false)
+ {
+ this->m_buffer = std::vector<float>(
+ this->m_params.m_frameLenPadded, 0.0);
+ this->m_frame = std::vector<float>(
+ this->m_params.m_frameLenPadded, 0.0);
+ this->m_melEnergies = std::vector<float>(
+ this->m_params.m_numFbankBins, 0.0);
+
+ this->m_windowFunc = std::vector<float>(this->m_params.m_frameLen);
+ const auto multiplier = static_cast<float>(2 * M_PI / this->m_params.m_frameLen);
+
+ /* Create window function. */
+ for (size_t i = 0; i < this->m_params.m_frameLen; ++i) {
+ this->m_windowFunc[i] = (0.5 - (0.5 *
+ math::MathUtils::CosineF32(static_cast<float>(i) * multiplier)));
+ }
+
+ math::MathUtils::FftInitF32(this->m_params.m_frameLenPadded, this->m_fftInstance);
+ debug("Instantiated Mel Spectrogram object: %s\n", this->m_params.Str().c_str());
+ }
+
+ void MelSpectrogram::Init()
+ {
+ this->InitMelFilterBank();
+ }
+
+ float MelSpectrogram::MelScale(const float freq, const bool useHTKMethod)
+ {
+ if (useHTKMethod) {
+ return 1127.0f * logf (1.0f + freq / 700.0f);
+ } else {
+ /* Slaney formula for mel scale. */
+ float mel = freq / ms_freqStep;
+
+ if (freq >= ms_minLogHz) {
+ mel = ms_minLogMel + logf(freq / ms_minLogHz) / ms_logStep;
+ }
+ return mel;
+ }
+ }
+
+ float MelSpectrogram::InverseMelScale(const float melFreq, const bool useHTKMethod)
+ {
+ if (useHTKMethod) {
+ return 700.0f * (expf (melFreq / 1127.0f) - 1.0f);
+ } else {
+ /* Slaney formula for inverse mel scale. */
+ float freq = ms_freqStep * melFreq;
+
+ if (melFreq >= ms_minLogMel) {
+ freq = ms_minLogHz * expf(ms_logStep * (melFreq - ms_minLogMel));
+ }
+ return freq;
+ }
+ }
+
+ bool MelSpectrogram::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();
+ float melEnergy = FLT_MIN; /* Avoid log of zero at later stages */
+ const uint32_t firstIndex = filterBankFilterFirst[bin];
+ const uint32_t lastIndex = std::min<int32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
+
+ for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
+ float energyRep = math::MathUtils::SqrtF32(fftVec[i]);
+ melEnergy += (*filterBankIter++ * energyRep);
+ }
+
+ melEnergies[bin] = melEnergy;
+ }
+
+ return true;
+ }
+
+ void MelSpectrogram::ConvertToLogarithmicScale(std::vector<float>& melEnergies)
+ {
+ for (float& melEnergy : melEnergies) {
+ melEnergy = logf(melEnergy);
+ }
+ }
+
+ void MelSpectrogram::ConvertToPowerSpectrum()
+ {
+ const uint32_t halfDim = this->m_buffer.size() / 2;
+
+ /* Handle this special case. */
+ float firstEnergy = this->m_buffer[0] * this->m_buffer[0];
+ float lastEnergy = this->m_buffer[1] * this->m_buffer[1];
+
+ math::MathUtils::ComplexMagnitudeSquaredF32(
+ this->m_buffer.data(),
+ this->m_buffer.size(),
+ this->m_buffer.data(),
+ this->m_buffer.size()/2);
+
+ this->m_buffer[0] = firstEnergy;
+ this->m_buffer[halfDim] = lastEnergy;
+ }
+
+ float MelSpectrogram::GetMelFilterBankNormaliser(
+ const float& leftMel,
+ const float& rightMel,
+ const bool useHTKMethod)
+ {
+ UNUSED(leftMel);
+ UNUSED(rightMel);
+ UNUSED(useHTKMethod);
+
+ /* By default, no normalisation => return 1 */
+ return 1.f;
+ }
+
+ void MelSpectrogram::InitMelFilterBank()
+ {
+ if (!this->IsMelFilterBankInited()) {
+ this->m_melFilterBank = this->CreateMelFilterBank();
+ this->m_filterBankInitialised = true;
+ }
+ }
+
+ bool MelSpectrogram::IsMelFilterBankInited() const
+ {
+ return this->m_filterBankInitialised;
+ }
+
+ std::vector<float> MelSpectrogram::ComputeMelSpec(const std::vector<int16_t>& audioData, float trainingMean)
+ {
+ this->InitMelFilterBank();
+
+ /* TensorFlow way of normalizing .wav data to (-1, 1). */
+ constexpr float normaliser = 1.0/(1<<15);
+ for (size_t i = 0; i < this->m_params.m_frameLen; ++i) {
+ this->m_frame[i] = static_cast<float>(audioData[i]) * normaliser;
+ }
+
+ /* Apply window function to input frame. */
+ for(size_t i = 0; i < this->m_params.m_frameLen; ++i) {
+ this->m_frame[i] *= this->m_windowFunc[i];
+ }
+
+ /* Set remaining frame values to 0. */
+ std::fill(this->m_frame.begin() + this->m_params.m_frameLen,this->m_frame.end(), 0);
+
+ /* Compute FFT. */
+ math::MathUtils::FftF32(this->m_frame, this->m_buffer, this->m_fftInstance);
+
+ /* Convert to power spectrum. */
+ this->ConvertToPowerSpectrum();
+
+ /* Apply mel filterbanks. */
+ if (!this->ApplyMelFilterBank(this->m_buffer,
+ this->m_melFilterBank,
+ this->m_filterBankFilterFirst,
+ this->m_filterBankFilterLast,
+ this->m_melEnergies)) {
+ printf_err("Failed to apply MEL filter banks\n");
+ }
+
+ /* Convert to logarithmic scale */
+ this->ConvertToLogarithmicScale(this->m_melEnergies);
+
+ /* Perform mean subtraction. */
+ for (auto& energy:this->m_melEnergies) {
+ energy -= trainingMean;
+ }
+
+ return this->m_melEnergies;
+ }
+
+ std::vector<std::vector<float>> MelSpectrogram::CreateMelFilterBank()
+ {
+ size_t numFftBins = this->m_params.m_frameLenPadded / 2;
+ float fftBinWidth = static_cast<float>(this->m_params.m_samplingFreq) / this->m_params.m_frameLenPadded;
+
+ float melLowFreq = MelSpectrogram::MelScale(this->m_params.m_melLoFreq,
+ this->m_params.m_useHtkMethod);
+ float melHighFreq = MelSpectrogram::MelScale(this->m_params.m_melHiFreq,
+ this->m_params.m_useHtkMethod);
+ float melFreqDelta = (melHighFreq - melLowFreq) / (this->m_params.m_numFbankBins + 1);
+
+ std::vector<float> thisBin = std::vector<float>(numFftBins);
+ std::vector<std::vector<float>> melFilterBank(
+ this->m_params.m_numFbankBins);
+ this->m_filterBankFilterFirst =
+ std::vector<uint32_t>(this->m_params.m_numFbankBins);
+ this->m_filterBankFilterLast =
+ std::vector<uint32_t>(this->m_params.m_numFbankBins);
+
+ for (size_t bin = 0; bin < this->m_params.m_numFbankBins; bin++) {
+ float leftMel = melLowFreq + bin * melFreqDelta;
+ float centerMel = melLowFreq + (bin + 1) * melFreqDelta;
+ float rightMel = melLowFreq + (bin + 2) * melFreqDelta;
+
+ uint32_t firstIndex = 0;
+ uint32_t lastIndex = 0;
+ bool firstIndexFound = false;
+ const float normaliser = this->GetMelFilterBankNormaliser(leftMel, rightMel, this->m_params.m_useHtkMethod);
+
+ for (size_t i = 0; i < numFftBins; ++i) {
+ float freq = (fftBinWidth * i); /* Center freq of this fft bin. */
+ float mel = MelSpectrogram::MelScale(freq, this->m_params.m_useHtkMethod);
+ thisBin[i] = 0.0;
+
+ if (mel > leftMel && mel < rightMel) {
+ float weight;
+ if (mel <= centerMel) {
+ weight = (mel - leftMel) / (centerMel - leftMel);
+ } else {
+ weight = (rightMel - mel) / (rightMel - centerMel);
+ }
+
+ thisBin[i] = weight * normaliser;
+ if (!firstIndexFound) {
+ firstIndex = i;
+ firstIndexFound = true;
+ }
+ lastIndex = i;
+ }
+ }
+
+ this->m_filterBankFilterFirst[bin] = firstIndex;
+ this->m_filterBankFilterLast[bin] = lastIndex;
+
+ /* Copy the part we care about. */
+ for (uint32_t i = firstIndex; i <= lastIndex; ++i) {
+ melFilterBank[bin].push_back(thisBin[i]);
+ }
+ }
+
+ return melFilterBank;
+ }
+
+} /* namespace audio */
+} /* namespace app */
+} /* namespace arm */