361 lines
18 KiB
C++
361 lines
18 KiB
C++
/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
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/*
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pYIN - A fundamental frequency estimator for monophonic audio
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Centre for Digital Music, Queen Mary, University of London.
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This program is free software; you can redistribute it and/or
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modify it under the terms of the GNU General Public License as
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published by the Free Software Foundation; either version 2 of the
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License, or (at your option) any later version. See the file
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COPYING included with this distribution for more information.
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*/
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#include "YinUtil.h"
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#include <vector>
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#include <cstdio>
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#include <cmath>
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#include <algorithm>
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void
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YinUtil::slowDifference(const double *in, double *yinBuffer, const size_t yinBufferSize)
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{
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yinBuffer[0] = 0;
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double delta ;
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int startPoint = 0;
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int endPoint = 0;
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for (size_t i = 1; i < yinBufferSize; ++i) {
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yinBuffer[i] = 0;
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startPoint = yinBufferSize/2 - i/2;
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endPoint = startPoint + yinBufferSize;
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for (int j = startPoint; j < endPoint; ++j) {
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delta = in[i+j] - in[j];
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yinBuffer[i] += delta * delta;
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}
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}
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}
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void
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YinUtil::fastDifference(const double *in, double *yinBuffer, const size_t yinBufferSize)
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{
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// DECLARE AND INITIALISE
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// initialisation of most of the arrays here was done in a separate function,
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// with all the arrays as members of the class... moved them back here.
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size_t frameSize = 2 * yinBufferSize;
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double *audioTransformedReal = new double[frameSize];
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double *audioTransformedImag = new double[frameSize];
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double *nullImag = new double[frameSize];
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double *kernel = new double[frameSize];
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double *kernelTransformedReal = new double[frameSize];
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double *kernelTransformedImag = new double[frameSize];
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double *yinStyleACFReal = new double[frameSize];
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double *yinStyleACFImag = new double[frameSize];
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double *powerTerms = new double[yinBufferSize];
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for (size_t j = 0; j < yinBufferSize; ++j)
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{
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yinBuffer[j] = 0.; // set to zero
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powerTerms[j] = 0.; // set to zero
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}
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for (size_t j = 0; j < frameSize; ++j)
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{
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nullImag[j] = 0.;
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audioTransformedReal[j] = 0.;
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audioTransformedImag[j] = 0.;
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kernel[j] = 0.;
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kernelTransformedReal[j] = 0.;
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kernelTransformedImag[j] = 0.;
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yinStyleACFReal[j] = 0.;
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yinStyleACFImag[j] = 0.;
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}
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// POWER TERM CALCULATION
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// ... for the power terms in equation (7) in the Yin paper
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powerTerms[0] = 0.0;
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for (size_t j = 0; j < yinBufferSize; ++j) {
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powerTerms[0] += in[j] * in[j];
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}
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// now iteratively calculate all others (saves a few multiplications)
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for (size_t tau = 1; tau < yinBufferSize; ++tau) {
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powerTerms[tau] = powerTerms[tau-1] - in[tau-1] * in[tau-1] + in[tau+yinBufferSize] * in[tau+yinBufferSize];
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}
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// YIN-STYLE AUTOCORRELATION via FFT
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// 1. data
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Vamp::FFT::forward(frameSize, in, nullImag, audioTransformedReal, audioTransformedImag);
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// 2. half of the data, disguised as a convolution kernel
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for (size_t j = 0; j < yinBufferSize; ++j) {
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kernel[j] = in[yinBufferSize-1-j];
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}
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Vamp::FFT::forward(frameSize, kernel, nullImag, kernelTransformedReal, kernelTransformedImag);
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// 3. convolution via complex multiplication -- written into
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for (size_t j = 0; j < frameSize; ++j) {
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yinStyleACFReal[j] = audioTransformedReal[j]*kernelTransformedReal[j] - audioTransformedImag[j]*kernelTransformedImag[j]; // real
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yinStyleACFImag[j] = audioTransformedReal[j]*kernelTransformedImag[j] + audioTransformedImag[j]*kernelTransformedReal[j]; // imaginary
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}
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Vamp::FFT::inverse(frameSize, yinStyleACFReal, yinStyleACFImag, audioTransformedReal, audioTransformedImag);
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// CALCULATION OF difference function
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// ... according to (7) in the Yin paper.
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for (size_t j = 0; j < yinBufferSize; ++j) {
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// taking only the real part
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yinBuffer[j] = powerTerms[0] + powerTerms[j] - 2 * audioTransformedReal[j+yinBufferSize-1];
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}
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delete [] audioTransformedReal;
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delete [] audioTransformedImag;
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delete [] nullImag;
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delete [] kernel;
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delete [] kernelTransformedReal;
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delete [] kernelTransformedImag;
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delete [] yinStyleACFReal;
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delete [] yinStyleACFImag;
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delete [] powerTerms;
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}
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void
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YinUtil::cumulativeDifference(double *yinBuffer, const size_t yinBufferSize)
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{
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size_t tau;
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yinBuffer[0] = 1;
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double runningSum = 0;
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for (tau = 1; tau < yinBufferSize; ++tau) {
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runningSum += yinBuffer[tau];
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if (runningSum == 0)
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{
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yinBuffer[tau] = 1;
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} else {
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yinBuffer[tau] *= tau / runningSum;
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}
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}
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}
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int
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YinUtil::absoluteThreshold(const double *yinBuffer, const size_t yinBufferSize, const double thresh)
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{
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size_t tau;
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size_t minTau = 0;
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double minVal = 1000.;
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// using Joren Six's "loop construct" from TarsosDSP
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tau = 2;
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while (tau < yinBufferSize)
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{
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if (yinBuffer[tau] < thresh)
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{
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while (tau+1 < yinBufferSize && yinBuffer[tau+1] < yinBuffer[tau])
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{
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++tau;
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}
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return tau;
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} else {
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if (yinBuffer[tau] < minVal)
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{
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minVal = yinBuffer[tau];
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minTau = tau;
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}
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}
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++tau;
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}
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if (minTau > 0)
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{
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return -minTau;
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}
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return 0;
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}
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static float uniformDist[100] = {0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000,0.0100000};
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static float betaDist1[100] = {0.028911,0.048656,0.061306,0.068539,0.071703,0.071877,0.069915,0.066489,0.062117,0.057199,0.052034,0.046844,0.041786,0.036971,0.032470,0.028323,0.024549,0.021153,0.018124,0.015446,0.013096,0.011048,0.009275,0.007750,0.006445,0.005336,0.004397,0.003606,0.002945,0.002394,0.001937,0.001560,0.001250,0.000998,0.000792,0.000626,0.000492,0.000385,0.000300,0.000232,0.000179,0.000137,0.000104,0.000079,0.000060,0.000045,0.000033,0.000024,0.000018,0.000013,0.000009,0.000007,0.000005,0.000003,0.000002,0.000002,0.000001,0.000001,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000};
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static float betaDist2[100] = {0.012614,0.022715,0.030646,0.036712,0.041184,0.044301,0.046277,0.047298,0.047528,0.047110,0.046171,0.044817,0.043144,0.041231,0.039147,0.036950,0.034690,0.032406,0.030133,0.027898,0.025722,0.023624,0.021614,0.019704,0.017900,0.016205,0.014621,0.013148,0.011785,0.010530,0.009377,0.008324,0.007366,0.006497,0.005712,0.005005,0.004372,0.003806,0.003302,0.002855,0.002460,0.002112,0.001806,0.001539,0.001307,0.001105,0.000931,0.000781,0.000652,0.000542,0.000449,0.000370,0.000303,0.000247,0.000201,0.000162,0.000130,0.000104,0.000082,0.000065,0.000051,0.000039,0.000030,0.000023,0.000018,0.000013,0.000010,0.000007,0.000005,0.000004,0.000003,0.000002,0.000001,0.000001,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000};
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static float betaDist3[100] = {0.006715,0.012509,0.017463,0.021655,0.025155,0.028031,0.030344,0.032151,0.033506,0.034458,0.035052,0.035331,0.035332,0.035092,0.034643,0.034015,0.033234,0.032327,0.031314,0.030217,0.029054,0.027841,0.026592,0.025322,0.024042,0.022761,0.021489,0.020234,0.019002,0.017799,0.016630,0.015499,0.014409,0.013362,0.012361,0.011407,0.010500,0.009641,0.008830,0.008067,0.007351,0.006681,0.006056,0.005475,0.004936,0.004437,0.003978,0.003555,0.003168,0.002814,0.002492,0.002199,0.001934,0.001695,0.001481,0.001288,0.001116,0.000963,0.000828,0.000708,0.000603,0.000511,0.000431,0.000361,0.000301,0.000250,0.000206,0.000168,0.000137,0.000110,0.000088,0.000070,0.000055,0.000043,0.000033,0.000025,0.000019,0.000014,0.000010,0.000007,0.000005,0.000004,0.000002,0.000002,0.000001,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000};
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static float betaDist4[100] = {0.003996,0.007596,0.010824,0.013703,0.016255,0.018501,0.020460,0.022153,0.023597,0.024809,0.025807,0.026607,0.027223,0.027671,0.027963,0.028114,0.028135,0.028038,0.027834,0.027535,0.027149,0.026687,0.026157,0.025567,0.024926,0.024240,0.023517,0.022763,0.021983,0.021184,0.020371,0.019548,0.018719,0.017890,0.017062,0.016241,0.015428,0.014627,0.013839,0.013068,0.012315,0.011582,0.010870,0.010181,0.009515,0.008874,0.008258,0.007668,0.007103,0.006565,0.006053,0.005567,0.005107,0.004673,0.004264,0.003880,0.003521,0.003185,0.002872,0.002581,0.002312,0.002064,0.001835,0.001626,0.001434,0.001260,0.001102,0.000959,0.000830,0.000715,0.000612,0.000521,0.000440,0.000369,0.000308,0.000254,0.000208,0.000169,0.000136,0.000108,0.000084,0.000065,0.000050,0.000037,0.000027,0.000019,0.000014,0.000009,0.000006,0.000004,0.000002,0.000001,0.000001,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000,0.000000};
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static float single10[100] = {0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,1.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000};
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static float single15[100] = {0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,1.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000};
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static float single20[100] = {0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,1.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000,0.00000};
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std::vector<double>
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YinUtil::yinProb(const double *yinBuffer, const size_t prior, const size_t yinBufferSize, const size_t minTau0, const size_t maxTau0)
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{
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size_t minTau = 2;
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size_t maxTau = yinBufferSize;
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// adapt period range, if necessary
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if (minTau0 > 0 && minTau0 < maxTau0) minTau = minTau0;
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if (maxTau0 > 0 && maxTau0 < yinBufferSize && maxTau0 > minTau) maxTau = maxTau0;
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double minWeight = 0.01;
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size_t tau;
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std::vector<float> thresholds;
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std::vector<float> distribution;
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std::vector<double> peakProb = std::vector<double>(yinBufferSize);
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size_t nThreshold = 100;
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int nThresholdInt = nThreshold;
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for (int i = 0; i < nThresholdInt; ++i)
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{
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switch (prior) {
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case 0:
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distribution.push_back(uniformDist[i]);
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break;
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case 1:
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distribution.push_back(betaDist1[i]);
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break;
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case 2:
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distribution.push_back(betaDist2[i]);
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break;
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case 3:
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distribution.push_back(betaDist3[i]);
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break;
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case 4:
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distribution.push_back(betaDist4[i]);
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break;
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case 5:
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distribution.push_back(single10[i]);
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break;
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case 6:
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distribution.push_back(single15[i]);
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break;
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case 7:
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distribution.push_back(single20[i]);
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break;
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default:
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distribution.push_back(uniformDist[i]);
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}
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thresholds.push_back(0.01 + i*0.01);
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}
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int currThreshInd = nThreshold-1;
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tau = minTau;
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// double factor = 1.0 / (0.25 * (nThresholdInt+1) * (nThresholdInt + 1)); // factor to scale down triangular weight
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size_t minInd = 0;
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float minVal = 42.f;
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// while (currThreshInd != -1 && tau < maxTau)
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// {
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// if (yinBuffer[tau] < thresholds[currThreshInd])
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// {
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// while (tau + 1 < maxTau && yinBuffer[tau+1] < yinBuffer[tau])
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// {
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// tau++;
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// }
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// // tau is now local minimum
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// // std::cerr << tau << " " << currThreshInd << " "<< thresholds[currThreshInd] << " " << distribution[currThreshInd] << std::endl;
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// if (yinBuffer[tau] < minVal && tau > 2){
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// minVal = yinBuffer[tau];
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// minInd = tau;
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// }
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// peakProb[tau] += distribution[currThreshInd];
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// currThreshInd--;
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// } else {
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// tau++;
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// }
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// }
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// double nonPeakProb = 1;
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// for (size_t i = minTau; i < maxTau; ++i)
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// {
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// nonPeakProb -= peakProb[i];
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// }
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//
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// std::cerr << tau << " " << currThreshInd << " "<< thresholds[currThreshInd] << " " << distribution[currThreshInd] << std::endl;
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float sumProb = 0;
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while (tau+1 < maxTau)
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|
{
|
|
if (yinBuffer[tau] < thresholds[thresholds.size()-1] && yinBuffer[tau+1] < yinBuffer[tau])
|
|
{
|
|
while (tau + 1 < maxTau && yinBuffer[tau+1] < yinBuffer[tau])
|
|
{
|
|
tau++;
|
|
}
|
|
// tau is now local minimum
|
|
// std::cerr << tau << " " << currThreshInd << " "<< thresholds[currThreshInd] << " " << distribution[currThreshInd] << std::endl;
|
|
if (yinBuffer[tau] < minVal && tau > 2){
|
|
minVal = yinBuffer[tau];
|
|
minInd = tau;
|
|
}
|
|
currThreshInd = nThresholdInt-1;
|
|
while (thresholds[currThreshInd] > yinBuffer[tau] && currThreshInd > -1) {
|
|
// std::cerr << distribution[currThreshInd] << std::endl;
|
|
peakProb[tau] += distribution[currThreshInd];
|
|
currThreshInd--;
|
|
}
|
|
// peakProb[tau] = 1 - yinBuffer[tau];
|
|
sumProb += peakProb[tau];
|
|
tau++;
|
|
} else {
|
|
tau++;
|
|
}
|
|
}
|
|
|
|
if (peakProb[minInd] > 1) {
|
|
std::cerr << "WARNING: yin has prob > 1 ??? I'm returning all zeros instead." << std::endl;
|
|
return(std::vector<double>(yinBufferSize));
|
|
}
|
|
|
|
double nonPeakProb = 1;
|
|
if (sumProb > 0) {
|
|
for (size_t i = minTau; i < maxTau; ++i)
|
|
{
|
|
peakProb[i] = peakProb[i] / sumProb * peakProb[minInd];
|
|
nonPeakProb -= peakProb[i];
|
|
}
|
|
}
|
|
if (minInd > 0)
|
|
{
|
|
// std::cerr << "min set " << minVal << " " << minInd << " " << nonPeakProb << std::endl;
|
|
peakProb[minInd] += nonPeakProb * minWeight;
|
|
}
|
|
|
|
return peakProb;
|
|
}
|
|
|
|
double
|
|
YinUtil::parabolicInterpolation(const double *yinBuffer, const size_t tau, const size_t yinBufferSize)
|
|
{
|
|
// this is taken almost literally from Joren Six's Java implementation
|
|
if (tau == yinBufferSize) // not valid anyway.
|
|
{
|
|
return static_cast<double>(tau);
|
|
}
|
|
|
|
double betterTau = 0.0;
|
|
if (tau > 0 && tau < yinBufferSize-1) {
|
|
float s0, s1, s2;
|
|
s0 = yinBuffer[tau-1];
|
|
s1 = yinBuffer[tau];
|
|
s2 = yinBuffer[tau+1];
|
|
|
|
double adjustment = (s2 - s0) / (2 * (2 * s1 - s2 - s0));
|
|
|
|
if (abs(adjustment)>1) adjustment = 0;
|
|
|
|
betterTau = tau + adjustment;
|
|
} else {
|
|
// std::cerr << "WARNING: can't do interpolation at the edge (tau = " << tau << "), will return un-interpolated value.\n";
|
|
betterTau = tau;
|
|
}
|
|
return betterTau;
|
|
}
|
|
|
|
double
|
|
YinUtil::sumSquare(const double *in, const size_t start, const size_t end)
|
|
{
|
|
double out = 0;
|
|
for (size_t i = start; i < end; ++i)
|
|
{
|
|
out += in[i] * in[i];
|
|
}
|
|
return out;
|
|
}
|