352 lines
11 KiB
C++
352 lines
11 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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QM DSP Library
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Centre for Digital Music, Queen Mary, University of London.
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This file 2005-2006 Christian Landone.
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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 "ConstantQ.h"
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#include "dsp/transforms/FFT.h"
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#include <iostream>
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#ifdef NOT_DEFINED
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// see note in CQprecalc
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#include "CQprecalc.cpp"
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static bool push_precalculated(int uk, int fftlength,
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std::vector<unsigned> &is,
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std::vector<unsigned> &js,
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std::vector<double> &real,
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std::vector<double> &imag)
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{
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if (uk == 76 && fftlength == 16384) {
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push_76_16384(is, js, real, imag);
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return true;
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}
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if (uk == 144 && fftlength == 4096) {
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push_144_4096(is, js, real, imag);
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return true;
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}
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if (uk == 65 && fftlength == 2048) {
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push_65_2048(is, js, real, imag);
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return true;
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}
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if (uk == 84 && fftlength == 65536) {
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push_84_65536(is, js, real, imag);
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return true;
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}
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return false;
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}
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#endif
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//---------------------------------------------------------------------------
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// nextpow2 returns the smallest integer n such that 2^n >= x.
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static double nextpow2(double x) {
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double y = ceil(log(x)/log(2.0));
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return(y);
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}
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static double squaredModule(const double & xx, const double & yy) {
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return xx*xx + yy*yy;
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}
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//----------------------------------------------------------------------------
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ConstantQ::ConstantQ( CQConfig Config ) :
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m_sparseKernel(0)
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{
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initialise( Config );
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}
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ConstantQ::~ConstantQ()
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{
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deInitialise();
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}
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//----------------------------------------------------------------------------
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void ConstantQ::sparsekernel()
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{
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// std::cerr << "ConstantQ: initialising sparse kernel, uK = " << m_uK << ", FFTLength = " << m_FFTLength << "...";
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SparseKernel *sk = new SparseKernel();
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#ifdef NOT_DEFINED
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if (push_precalculated(m_uK, m_FFTLength,
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sk->is, sk->js, sk->real, sk->imag)) {
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// std::cerr << "using precalculated kernel" << std::endl;
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m_sparseKernel = sk;
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return;
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}
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#endif
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//generates spectral kernel matrix (upside down?)
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// initialise temporal kernel with zeros, twice length to deal w. complex numbers
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double* hammingWindowRe = new double [ m_FFTLength ];
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double* hammingWindowIm = new double [ m_FFTLength ];
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double* transfHammingWindowRe = new double [ m_FFTLength ];
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double* transfHammingWindowIm = new double [ m_FFTLength ];
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for (unsigned u=0; u < m_FFTLength; u++)
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{
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hammingWindowRe[u] = 0;
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hammingWindowIm[u] = 0;
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}
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// Here, fftleng*2 is a guess of the number of sparse cells in the matrix
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// The matrix K x fftlength but the non-zero cells are an antialiased
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// square root function. So mostly is a line, with some grey point.
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sk->is.reserve( m_FFTLength*2 );
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sk->js.reserve( m_FFTLength*2 );
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sk->real.reserve( m_FFTLength*2 );
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sk->imag.reserve( m_FFTLength*2 );
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// for each bin value K, calculate temporal kernel, take its fft to
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//calculate the spectral kernel then threshold it to make it sparse and
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//add it to the sparse kernels matrix
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double squareThreshold = m_CQThresh * m_CQThresh;
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FFT m_FFT(m_FFTLength);
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for (unsigned k = m_uK; k--; )
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{
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for (unsigned u=0; u < m_FFTLength; u++)
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{
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hammingWindowRe[u] = 0;
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hammingWindowIm[u] = 0;
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}
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// Computing a hamming window
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const unsigned hammingLength = (int) ceil( m_dQ * m_FS / ( m_FMin * pow(2,((double)(k))/(double)m_BPO)));
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unsigned origin = m_FFTLength/2 - hammingLength/2;
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for (unsigned i=0; i<hammingLength; i++)
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{
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const double angle = 2*PI*m_dQ*i/hammingLength;
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const double real = cos(angle);
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const double imag = sin(angle);
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const double absol = hamming(hammingLength, i)/hammingLength;
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hammingWindowRe[ origin + i ] = absol*real;
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hammingWindowIm[ origin + i ] = absol*imag;
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}
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for (unsigned i = 0; i < m_FFTLength/2; ++i) {
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double temp = hammingWindowRe[i];
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hammingWindowRe[i] = hammingWindowRe[i + m_FFTLength/2];
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hammingWindowRe[i + m_FFTLength/2] = temp;
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temp = hammingWindowIm[i];
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hammingWindowIm[i] = hammingWindowIm[i + m_FFTLength/2];
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hammingWindowIm[i + m_FFTLength/2] = temp;
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}
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//do fft of hammingWindow
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m_FFT.process( 0, hammingWindowRe, hammingWindowIm, transfHammingWindowRe, transfHammingWindowIm );
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for (unsigned j=0; j<( m_FFTLength ); j++)
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{
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// perform thresholding
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const double squaredBin = squaredModule( transfHammingWindowRe[ j ], transfHammingWindowIm[ j ]);
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if (squaredBin <= squareThreshold) continue;
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// Insert non-zero position indexes, doubled because they are floats
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sk->is.push_back(j);
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sk->js.push_back(k);
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// take conjugate, normalise and add to array sparkernel
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sk->real.push_back( transfHammingWindowRe[ j ]/m_FFTLength);
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sk->imag.push_back(-transfHammingWindowIm[ j ]/m_FFTLength);
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}
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}
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delete [] hammingWindowRe;
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delete [] hammingWindowIm;
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delete [] transfHammingWindowRe;
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delete [] transfHammingWindowIm;
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/*
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using std::cout;
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using std::endl;
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cout.precision(28);
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int n = sk->is.size();
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int w = 8;
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cout << "static unsigned int sk_i_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
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for (int i = 0; i < n; ++i) {
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if (i % w == 0) cout << " ";
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cout << sk->is[i];
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if (i + 1 < n) cout << ", ";
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if (i % w == w-1) cout << endl;
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};
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if (n % w != 0) cout << endl;
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cout << "};" << endl;
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n = sk->js.size();
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cout << "static unsigned int sk_j_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
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for (int i = 0; i < n; ++i) {
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if (i % w == 0) cout << " ";
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cout << sk->js[i];
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if (i + 1 < n) cout << ", ";
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if (i % w == w-1) cout << endl;
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};
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if (n % w != 0) cout << endl;
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cout << "};" << endl;
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w = 2;
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n = sk->real.size();
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cout << "static double sk_real_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
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for (int i = 0; i < n; ++i) {
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if (i % w == 0) cout << " ";
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cout << sk->real[i];
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if (i + 1 < n) cout << ", ";
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if (i % w == w-1) cout << endl;
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};
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if (n % w != 0) cout << endl;
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cout << "};" << endl;
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n = sk->imag.size();
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cout << "static double sk_imag_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
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for (int i = 0; i < n; ++i) {
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if (i % w == 0) cout << " ";
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cout << sk->imag[i];
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if (i + 1 < n) cout << ", ";
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if (i % w == w-1) cout << endl;
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};
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if (n % w != 0) cout << endl;
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cout << "};" << endl;
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cout << "static void push_" << m_uK << "_" << m_FFTLength << "(vector<unsigned int> &is, vector<unsigned int> &js, vector<double> &real, vector<double> &imag)" << endl;
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cout << "{\n is.reserve(" << n << ");\n";
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cout << " js.reserve(" << n << ");\n";
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cout << " real.reserve(" << n << ");\n";
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cout << " imag.reserve(" << n << ");\n";
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cout << " for (int i = 0; i < " << n << "; ++i) {" << endl;
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cout << " is.push_back(sk_i_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
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cout << " js.push_back(sk_j_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
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cout << " real.push_back(sk_real_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
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cout << " imag.push_back(sk_imag_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
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cout << " }" << endl;
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cout << "}" << endl;
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*/
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// std::cerr << "done\n -> is: " << sk->is.size() << ", js: " << sk->js.size() << ", reals: " << sk->real.size() << ", imags: " << sk->imag.size() << std::endl;
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m_sparseKernel = sk;
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return;
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}
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//-----------------------------------------------------------------------------
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double* ConstantQ::process( const double* fftdata )
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{
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if (!m_sparseKernel) {
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std::cerr << "ERROR: ConstantQ::process: Sparse kernel has not been initialised" << std::endl;
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return m_CQdata;
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}
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SparseKernel *sk = m_sparseKernel;
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for (unsigned row=0; row<2*m_uK; row++)
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{
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m_CQdata[ row ] = 0;
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m_CQdata[ row+1 ] = 0;
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}
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const unsigned *fftbin = &(sk->is[0]);
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const unsigned *cqbin = &(sk->js[0]);
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const double *real = &(sk->real[0]);
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const double *imag = &(sk->imag[0]);
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const unsigned int sparseCells = sk->real.size();
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for (unsigned i = 0; i<sparseCells; i++)
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{
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const unsigned row = cqbin[i];
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const unsigned col = fftbin[i];
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const double & r1 = real[i];
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const double & i1 = imag[i];
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const double & r2 = fftdata[ (2*m_FFTLength) - 2*col - 2 ];
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const double & i2 = fftdata[ (2*m_FFTLength) - 2*col - 2 + 1 ];
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// add the multiplication
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m_CQdata[ 2*row ] += (r1*r2 - i1*i2);
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m_CQdata[ 2*row+1] += (r1*i2 + i1*r2);
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}
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return m_CQdata;
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}
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void ConstantQ::initialise( CQConfig Config )
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{
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m_FS = Config.FS;
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m_FMin = Config.min; // min freq
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m_FMax = Config.max; // max freq
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m_BPO = Config.BPO; // bins per octave
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m_CQThresh = Config.CQThresh;// ConstantQ threshold for kernel generation
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m_dQ = 1/(pow(2,(1/(double)m_BPO))-1); // Work out Q value for Filter bank
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m_uK = (unsigned int) ceil(m_BPO * log(m_FMax/m_FMin)/log(2.0)); // No. of constant Q bins
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// std::cerr << "ConstantQ::initialise: rate = " << m_FS << ", fmin = " << m_FMin << ", fmax = " << m_FMax << ", bpo = " << m_BPO << ", K = " << m_uK << ", Q = " << m_dQ << std::endl;
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// work out length of fft required for this constant Q Filter bank
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m_FFTLength = (int) pow(2, nextpow2(ceil( m_dQ*m_FS/m_FMin )));
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m_hop = m_FFTLength/8; // <------ hop size is window length divided by 32
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// std::cerr << "ConstantQ::initialise: -> fft length = " << m_FFTLength << ", hop = " << m_hop << std::endl;
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// allocate memory for cqdata
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m_CQdata = new double [2*m_uK];
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}
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void ConstantQ::deInitialise()
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{
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delete [] m_CQdata;
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delete m_sparseKernel;
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}
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void ConstantQ::process(const double *FFTRe, const double* FFTIm,
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double *CQRe, double *CQIm)
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{
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if (!m_sparseKernel) {
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std::cerr << "ERROR: ConstantQ::process: Sparse kernel has not been initialised" << std::endl;
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return;
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}
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SparseKernel *sk = m_sparseKernel;
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for (unsigned row=0; row<m_uK; row++)
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{
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CQRe[ row ] = 0;
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CQIm[ row ] = 0;
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}
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const unsigned *fftbin = &(sk->is[0]);
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const unsigned *cqbin = &(sk->js[0]);
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const double *real = &(sk->real[0]);
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const double *imag = &(sk->imag[0]);
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const unsigned int sparseCells = sk->real.size();
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for (unsigned i = 0; i<sparseCells; i++)
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{
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const unsigned row = cqbin[i];
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const unsigned col = fftbin[i];
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const double & r1 = real[i];
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const double & i1 = imag[i];
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const double & r2 = FFTRe[ m_FFTLength - col - 1 ];
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const double & i2 = FFTIm[ m_FFTLength - col - 1 ];
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// add the multiplication
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CQRe[ row ] += (r1*r2 - i1*i2);
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CQIm[ row ] += (r1*i2 + i1*r2);
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}
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}
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