| // |
| // Copyright © 2020 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include <algorithm> |
| #include <numeric> |
| #include <math.h> |
| #include <string.h> |
| |
| #include "MathUtils.hpp" |
| #include "Preprocess.hpp" |
| |
| Preprocess::Preprocess( |
| const uint32_t windowLen, |
| const uint32_t windowStride, |
| const MFCC mfccInst): |
| _m_mfcc(mfccInst), |
| _m_mfccBuf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors), |
| _m_delta1Buf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors), |
| _m_delta2Buf(mfccInst._m_params.m_numMfccFeatures, mfccInst._m_params.m_numMfccVectors), |
| _m_windowLen(windowLen), |
| _m_windowStride(windowStride) |
| { |
| if (mfccInst._m_params.m_numMfccFeatures > 0 && windowLen > 0) |
| { |
| this->_m_mfcc.Init(); |
| } |
| } |
| |
| Preprocess::~Preprocess() |
| { |
| } |
| |
| bool Preprocess::Invoke( const float* audioData, const uint32_t audioDataLen, std::vector<int8_t>& output, |
| int quantOffset, float quantScale) |
| { |
| this->_m_window = SlidingWindow<const float>( |
| audioData, audioDataLen, |
| this->_m_windowLen, this->_m_windowStride); |
| |
| uint32_t mfccBufIdx = 0; |
| |
| // Init buffers with 0 |
| std::fill(_m_mfccBuf.begin(), _m_mfccBuf.end(), 0.f); |
| std::fill(_m_delta1Buf.begin(), _m_delta1Buf.end(), 0.f); |
| std::fill(_m_delta2Buf.begin(), _m_delta2Buf.end(), 0.f); |
| |
| /* While we can slide over the window */ |
| while (this->_m_window.HasNext()) |
| { |
| const float* mfccWindow = this->_m_window.Next(); |
| auto mfccAudioData = std::vector<float>( |
| mfccWindow, |
| mfccWindow + this->_m_windowLen); |
| |
| auto mfcc = this->_m_mfcc.MfccCompute(mfccAudioData); |
| for (size_t i = 0; i < this->_m_mfccBuf.size(0); ++i) |
| { |
| this->_m_mfccBuf(i, mfccBufIdx) = mfcc[i]; |
| } |
| ++mfccBufIdx; |
| } |
| |
| /* Pad MFCC if needed by repeating last feature vector */ |
| while (mfccBufIdx != this->_m_mfcc._m_params.m_numMfccVectors) |
| { |
| memcpy(&this->_m_mfccBuf(0, mfccBufIdx), |
| &this->_m_mfccBuf(0, mfccBufIdx-1), sizeof(float)*this->_m_mfcc._m_params.m_numMfccFeatures); |
| ++mfccBufIdx; |
| } |
| |
| /* Compute first and second order deltas from MFCCs */ |
| this->_ComputeDeltas(this->_m_mfccBuf, |
| this->_m_delta1Buf, |
| this->_m_delta2Buf); |
| |
| /* Normalise */ |
| this->_Normalise(); |
| |
| return this->_Quantise<int8_t>(output.data(), quantOffset, quantScale); |
| } |
| |
| bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc, |
| Array2d<float>& delta1, |
| Array2d<float>& delta2) |
| { |
| const std::vector <float> delta1Coeffs = |
| {6.66666667e-02, 5.00000000e-02, 3.33333333e-02, |
| 1.66666667e-02, -3.46944695e-18, -1.66666667e-02, |
| -3.33333333e-02, -5.00000000e-02, -6.66666667e-02}; |
| |
| const std::vector <float> delta2Coeffs = |
| {0.06060606, 0.01515152, -0.01731602, |
| -0.03679654, -0.04329004, -0.03679654, |
| -0.01731602, 0.01515152, 0.06060606}; |
| |
| if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) || |
| mfcc.size(0) == 0 || mfcc.size(1) == 0) |
| { |
| return false; |
| } |
| |
| /* Get the middle index; coeff vec len should always be odd */ |
| const size_t coeffLen = delta1Coeffs.size(); |
| const size_t fMidIdx = (coeffLen - 1)/2; |
| const size_t numFeatures = mfcc.size(0); |
| const size_t numFeatVectors = mfcc.size(1); |
| |
| /* iterate through features in MFCC vector*/ |
| for (size_t i = 0; i < numFeatures; ++i) |
| { |
| /* for each feature, iterate through time (t) samples representing feature evolution and |
| * calculate d/dt and d^2/dt^2, using 1d convolution with differential kernels. |
| * Convolution padding = valid, result size is `time length - kernel length + 1`. |
| * The result is padded with 0 from both sides to match the size of initial time samples data. |
| * |
| * For the small filter, conv1d implementation as a simple loop is efficient enough. |
| * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32. |
| */ |
| |
| for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j) |
| { |
| float d1 = 0; |
| float d2 = 0; |
| const size_t mfccStIdx = j - fMidIdx; |
| |
| for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m) |
| { |
| |
| d1 += mfcc(i,mfccStIdx + k) * delta1Coeffs[m]; |
| d2 += mfcc(i,mfccStIdx + k) * delta2Coeffs[m]; |
| } |
| |
| delta1(i,j) = d1; |
| delta2(i,j) = d2; |
| } |
| } |
| |
| return true; |
| } |
| |
| float Preprocess::_GetMean(Array2d<float>& vec) |
| { |
| return MathUtils::MeanF32(vec.begin(), vec.totalSize()); |
| } |
| |
| float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean) |
| { |
| return MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean); |
| } |
| |
| void Preprocess::_NormaliseVec(Array2d<float>& vec) |
| { |
| auto mean = Preprocess::_GetMean(vec); |
| auto stddev = Preprocess::_GetStdDev(vec, mean); |
| |
| if (stddev == 0) |
| { |
| std::fill(vec.begin(), vec.end(), 0); |
| } |
| else |
| { |
| const float stddevInv = 1.f/stddev; |
| const float normalisedMean = mean/stddev; |
| |
| auto NormalisingFunction = [=](float &value) { |
| value = value * stddevInv - normalisedMean; |
| }; |
| std::for_each(vec.begin(), vec.end(), NormalisingFunction); |
| } |
| } |
| |
| void Preprocess::_Normalise() |
| { |
| Preprocess::_NormaliseVec(this->_m_mfccBuf); |
| Preprocess::_NormaliseVec(this->_m_delta1Buf); |
| Preprocess::_NormaliseVec(this->_m_delta2Buf); |
| } |
| |
| float Preprocess::_GetQuantElem( |
| const float elem, |
| const float quantScale, |
| const int quantOffset, |
| const float minVal, |
| const float maxVal) |
| { |
| float val = std::round((elem/quantScale) + quantOffset); |
| float maxim = std::max<float>(val, minVal); |
| float returnVal = std::min<float>(std::max<float>(val, minVal), maxVal); |
| return returnVal; |
| } |