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/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 */