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livetrax/libs/qm-dsp/dsp/chromagram/ConstantQ.cpp

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/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
/*
QM DSP Library
Centre for Digital Music, Queen Mary, University of London.
This file 2005-2006 Christian Landone.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of the
License, or (at your option) any later version. See the file
COPYING included with this distribution for more information.
*/
#include "ConstantQ.h"
#include "dsp/transforms/FFT.h"
#include <iostream>
#ifdef NOT_DEFINED
// see note in CQprecalc
#include "CQprecalc.cpp"
static bool push_precalculated(int uk, int fftlength,
std::vector<unsigned> &is,
std::vector<unsigned> &js,
std::vector<double> &real,
std::vector<double> &imag)
{
if (uk == 76 && fftlength == 16384) {
push_76_16384(is, js, real, imag);
return true;
}
if (uk == 144 && fftlength == 4096) {
push_144_4096(is, js, real, imag);
return true;
}
if (uk == 65 && fftlength == 2048) {
push_65_2048(is, js, real, imag);
return true;
}
if (uk == 84 && fftlength == 65536) {
push_84_65536(is, js, real, imag);
return true;
}
return false;
}
#endif
//---------------------------------------------------------------------------
// nextpow2 returns the smallest integer n such that 2^n >= x.
static double nextpow2(double x) {
double y = ceil(log(x)/log(2.0));
return(y);
}
static double squaredModule(const double & xx, const double & yy) {
return xx*xx + yy*yy;
}
//----------------------------------------------------------------------------
ConstantQ::ConstantQ( CQConfig Config ) :
m_sparseKernel(0)
{
initialise( Config );
}
ConstantQ::~ConstantQ()
{
deInitialise();
}
//----------------------------------------------------------------------------
void ConstantQ::sparsekernel()
{
// std::cerr << "ConstantQ: initialising sparse kernel, uK = " << m_uK << ", FFTLength = " << m_FFTLength << "...";
SparseKernel *sk = new SparseKernel();
#ifdef NOT_DEFINED
if (push_precalculated(m_uK, m_FFTLength,
sk->is, sk->js, sk->real, sk->imag)) {
// std::cerr << "using precalculated kernel" << std::endl;
m_sparseKernel = sk;
return;
}
#endif
//generates spectral kernel matrix (upside down?)
// initialise temporal kernel with zeros, twice length to deal w. complex numbers
double* hammingWindowRe = new double [ m_FFTLength ];
double* hammingWindowIm = new double [ m_FFTLength ];
double* transfHammingWindowRe = new double [ m_FFTLength ];
double* transfHammingWindowIm = new double [ m_FFTLength ];
for (unsigned u=0; u < m_FFTLength; u++)
{
hammingWindowRe[u] = 0;
hammingWindowIm[u] = 0;
}
// Here, fftleng*2 is a guess of the number of sparse cells in the matrix
// The matrix K x fftlength but the non-zero cells are an antialiased
// square root function. So mostly is a line, with some grey point.
sk->is.reserve( m_FFTLength*2 );
sk->js.reserve( m_FFTLength*2 );
sk->real.reserve( m_FFTLength*2 );
sk->imag.reserve( m_FFTLength*2 );
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// for each bin value K, calculate temporal kernel, take its fft to
//calculate the spectral kernel then threshold it to make it sparse and
//add it to the sparse kernels matrix
double squareThreshold = m_CQThresh * m_CQThresh;
FFT m_FFT(m_FFTLength);
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for (unsigned k = m_uK; k--; )
{
for (unsigned u=0; u < m_FFTLength; u++)
{
hammingWindowRe[u] = 0;
hammingWindowIm[u] = 0;
}
// Computing a hamming window
const unsigned hammingLength = (int) ceil( m_dQ * m_FS / ( m_FMin * pow(2,((double)(k))/(double)m_BPO)));
unsigned origin = m_FFTLength/2 - hammingLength/2;
for (unsigned i=0; i<hammingLength; i++)
{
const double angle = 2*PI*m_dQ*i/hammingLength;
const double real = cos(angle);
const double imag = sin(angle);
const double absol = hamming(hammingLength, i)/hammingLength;
hammingWindowRe[ origin + i ] = absol*real;
hammingWindowIm[ origin + i ] = absol*imag;
}
for (unsigned i = 0; i < m_FFTLength/2; ++i) {
double temp = hammingWindowRe[i];
hammingWindowRe[i] = hammingWindowRe[i + m_FFTLength/2];
hammingWindowRe[i + m_FFTLength/2] = temp;
temp = hammingWindowIm[i];
hammingWindowIm[i] = hammingWindowIm[i + m_FFTLength/2];
hammingWindowIm[i + m_FFTLength/2] = temp;
}
//do fft of hammingWindow
m_FFT.process( 0, hammingWindowRe, hammingWindowIm, transfHammingWindowRe, transfHammingWindowIm );
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for (unsigned j=0; j<( m_FFTLength ); j++)
{
// perform thresholding
const double squaredBin = squaredModule( transfHammingWindowRe[ j ], transfHammingWindowIm[ j ]);
if (squaredBin <= squareThreshold) continue;
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// Insert non-zero position indexes, doubled because they are floats
sk->is.push_back(j);
sk->js.push_back(k);
// take conjugate, normalise and add to array sparkernel
sk->real.push_back( transfHammingWindowRe[ j ]/m_FFTLength);
sk->imag.push_back(-transfHammingWindowIm[ j ]/m_FFTLength);
}
}
delete [] hammingWindowRe;
delete [] hammingWindowIm;
delete [] transfHammingWindowRe;
delete [] transfHammingWindowIm;
/*
using std::cout;
using std::endl;
cout.precision(28);
int n = sk->is.size();
int w = 8;
cout << "static unsigned int sk_i_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
for (int i = 0; i < n; ++i) {
if (i % w == 0) cout << " ";
cout << sk->is[i];
if (i + 1 < n) cout << ", ";
if (i % w == w-1) cout << endl;
};
if (n % w != 0) cout << endl;
cout << "};" << endl;
n = sk->js.size();
cout << "static unsigned int sk_j_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
for (int i = 0; i < n; ++i) {
if (i % w == 0) cout << " ";
cout << sk->js[i];
if (i + 1 < n) cout << ", ";
if (i % w == w-1) cout << endl;
};
if (n % w != 0) cout << endl;
cout << "};" << endl;
w = 2;
n = sk->real.size();
cout << "static double sk_real_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
for (int i = 0; i < n; ++i) {
if (i % w == 0) cout << " ";
cout << sk->real[i];
if (i + 1 < n) cout << ", ";
if (i % w == w-1) cout << endl;
};
if (n % w != 0) cout << endl;
cout << "};" << endl;
n = sk->imag.size();
cout << "static double sk_imag_" << m_uK << "_" << m_FFTLength << "[" << n << "] = {" << endl;
for (int i = 0; i < n; ++i) {
if (i % w == 0) cout << " ";
cout << sk->imag[i];
if (i + 1 < n) cout << ", ";
if (i % w == w-1) cout << endl;
};
if (n % w != 0) cout << endl;
cout << "};" << endl;
cout << "static void push_" << m_uK << "_" << m_FFTLength << "(vector<unsigned int> &is, vector<unsigned int> &js, vector<double> &real, vector<double> &imag)" << endl;
cout << "{\n is.reserve(" << n << ");\n";
cout << " js.reserve(" << n << ");\n";
cout << " real.reserve(" << n << ");\n";
cout << " imag.reserve(" << n << ");\n";
cout << " for (int i = 0; i < " << n << "; ++i) {" << endl;
cout << " is.push_back(sk_i_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
cout << " js.push_back(sk_j_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
cout << " real.push_back(sk_real_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
cout << " imag.push_back(sk_imag_" << m_uK << "_" << m_FFTLength << "[i]);" << endl;
cout << " }" << endl;
cout << "}" << endl;
*/
// std::cerr << "done\n -> is: " << sk->is.size() << ", js: " << sk->js.size() << ", reals: " << sk->real.size() << ", imags: " << sk->imag.size() << std::endl;
m_sparseKernel = sk;
return;
}
//-----------------------------------------------------------------------------
double* ConstantQ::process( const double* fftdata )
{
if (!m_sparseKernel) {
std::cerr << "ERROR: ConstantQ::process: Sparse kernel has not been initialised" << std::endl;
return m_CQdata;
}
SparseKernel *sk = m_sparseKernel;
for (unsigned row=0; row<2*m_uK; row++)
{
m_CQdata[ row ] = 0;
m_CQdata[ row+1 ] = 0;
}
const unsigned *fftbin = &(sk->is[0]);
const unsigned *cqbin = &(sk->js[0]);
const double *real = &(sk->real[0]);
const double *imag = &(sk->imag[0]);
const unsigned int sparseCells = sk->real.size();
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for (unsigned i = 0; i<sparseCells; i++)
{
const unsigned row = cqbin[i];
const unsigned col = fftbin[i];
const double & r1 = real[i];
const double & i1 = imag[i];
const double & r2 = fftdata[ (2*m_FFTLength) - 2*col - 2 ];
const double & i2 = fftdata[ (2*m_FFTLength) - 2*col - 2 + 1 ];
// add the multiplication
m_CQdata[ 2*row ] += (r1*r2 - i1*i2);
m_CQdata[ 2*row+1] += (r1*i2 + i1*r2);
}
return m_CQdata;
}
void ConstantQ::initialise( CQConfig Config )
{
m_FS = Config.FS;
m_FMin = Config.min; // min freq
m_FMax = Config.max; // max freq
m_BPO = Config.BPO; // bins per octave
m_CQThresh = Config.CQThresh;// ConstantQ threshold for kernel generation
m_dQ = 1/(pow(2,(1/(double)m_BPO))-1); // Work out Q value for Filter bank
m_uK = (unsigned int) ceil(m_BPO * log(m_FMax/m_FMin)/log(2.0)); // No. of constant Q bins
// std::cerr << "ConstantQ::initialise: rate = " << m_FS << ", fmin = " << m_FMin << ", fmax = " << m_FMax << ", bpo = " << m_BPO << ", K = " << m_uK << ", Q = " << m_dQ << std::endl;
// work out length of fft required for this constant Q Filter bank
m_FFTLength = (int) pow(2, nextpow2(ceil( m_dQ*m_FS/m_FMin )));
m_hop = m_FFTLength/8; // <------ hop size is window length divided by 32
// std::cerr << "ConstantQ::initialise: -> fft length = " << m_FFTLength << ", hop = " << m_hop << std::endl;
// allocate memory for cqdata
m_CQdata = new double [2*m_uK];
}
void ConstantQ::deInitialise()
{
delete [] m_CQdata;
delete m_sparseKernel;
}
void ConstantQ::process(const double *FFTRe, const double* FFTIm,
double *CQRe, double *CQIm)
{
if (!m_sparseKernel) {
std::cerr << "ERROR: ConstantQ::process: Sparse kernel has not been initialised" << std::endl;
return;
}
SparseKernel *sk = m_sparseKernel;
for (unsigned row=0; row<m_uK; row++)
{
CQRe[ row ] = 0;
CQIm[ row ] = 0;
}
const unsigned *fftbin = &(sk->is[0]);
const unsigned *cqbin = &(sk->js[0]);
const double *real = &(sk->real[0]);
const double *imag = &(sk->imag[0]);
const unsigned int sparseCells = sk->real.size();
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for (unsigned i = 0; i<sparseCells; i++)
{
const unsigned row = cqbin[i];
const unsigned col = fftbin[i];
const double & r1 = real[i];
const double & i1 = imag[i];
const double & r2 = FFTRe[ m_FFTLength - col - 1 ];
const double & i2 = FFTIm[ m_FFTLength - col - 1 ];
// add the multiplication
CQRe[ row ] += (r1*r2 - i1*i2);
CQIm[ row ] += (r1*i2 + i1*r2);
}
}