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//
// 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;
}
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