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livetrax/libs/qm-dsp/dsp/segmentation/cluster_segmenter.c

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/*
* cluster_segmenter.c
* soundbite
*
* Created by Mark Levy on 06/04/2006.
* Copyright 2006 Centre for Digital Music, Queen Mary, University of London.
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 "cluster_segmenter.h"
extern int readmatarray_size(const char *filepath, int n_array, int* t, int* d);
extern int readmatarray(const char *filepath, int n_array, int t, int d, double** arr);
/* converts constant-Q features to normalised chroma */
void cq2chroma(double** cq, int nframes, int ncoeff, int bins, double** chroma)
{
int noct = ncoeff / bins; /* number of complete octaves in constant-Q */
int t, b, oct, ix;
//double maxchroma; /* max chroma value at each time, for normalisation */
//double sum; /* for normalisation */
for (t = 0; t < nframes; t++)
{
for (b = 0; b < bins; b++)
chroma[t][b] = 0;
for (oct = 0; oct < noct; oct++)
{
ix = oct * bins;
for (b = 0; b < bins; b++)
chroma[t][b] += fabs(cq[t][ix+b]);
}
/* normalise to unit sum
sum = 0;
for (b = 0; b < bins; b++)
sum += chroma[t][b];
for (b = 0; b < bins; b++)
chroma[t][b] /= sum;
*/
/* normalise to unit max - NO this made results much worse!
maxchroma = 0;
for (b = 0; b < bins; b++)
if (chroma[t][b] > maxchroma)
maxchroma = chroma[t][b];
if (maxchroma > 0)
for (b = 0; b < bins; b++)
chroma[t][b] /= maxchroma;
*/
}
}
/* applies MPEG-7 normalisation to constant-Q features, storing normalised envelope (norm) in last feature dimension */
void mpeg7_constq(double** features, int nframes, int ncoeff)
{
int i, j;
double ss;
double env;
double maxenv = 0;
/* convert const-Q features to dB scale */
for (i = 0; i < nframes; i++)
for (j = 0; j < ncoeff; j++)
features[i][j] = 10.0 * log10(features[i][j]+DBL_EPSILON);
/* normalise each feature vector and add the norm as an extra feature dimension */
for (i = 0; i < nframes; i++)
{
ss = 0;
for (j = 0; j < ncoeff; j++)
ss += features[i][j] * features[i][j];
env = sqrt(ss);
for (j = 0; j < ncoeff; j++)
features[i][j] /= env;
features[i][ncoeff] = env;
if (env > maxenv)
maxenv = env;
}
/* normalise the envelopes */
for (i = 0; i < nframes; i++)
features[i][ncoeff] /= maxenv;
}
/* return histograms h[nx*m] of data x[nx] into m bins using a sliding window of length h_len (MUST BE ODD) */
/* NB h is a vector in row major order, as required by cluster_melt() */
/* for historical reasons we normalise the histograms by their norm (not to sum to one) */
void create_histograms(int* x, int nx, int m, int hlen, double* h)
{
int i, j, t;
double norm;
for (i = 0; i < nx*m; i++)
h[i] = 0;
for (i = hlen/2; i < nx-hlen/2; i++)
{
for (j = 0; j < m; j++)
h[i*m+j] = 0;
for (t = i-hlen/2; t <= i+hlen/2; t++)
++h[i*m+x[t]];
norm = 0;
for (j = 0; j < m; j++)
norm += h[i*m+j] * h[i*m+j];
for (j = 0; j < m; j++)
h[i*m+j] /= norm;
}
/* duplicate histograms at beginning and end to create one histogram for each data value supplied */
for (i = 0; i < hlen/2; i++)
for (j = 0; j < m; j++)
h[i*m+j] = h[hlen/2*m+j];
for (i = nx-hlen/2; i < nx; i++)
for (j = 0; j < m; j++)
h[i*m+j] = h[(nx-hlen/2-1)*m+j];
}
/* segment using HMM and then histogram clustering */
void cluster_segment(int* q, double** features, int frames_read, int feature_length, int nHMM_states,
int histogram_length, int nclusters, int neighbour_limit)
{
int i, j;
/*****************************/
if (0) {
/* try just using the predominant bin number as a 'decoded state' */
nHMM_states = feature_length + 1; /* allow a 'zero' state */
double chroma_thresh = 0.05;
double maxval;
int maxbin;
for (i = 0; i < frames_read; i++)
{
maxval = 0;
for (j = 0; j < feature_length; j++)
{
if (features[i][j] > maxval)
{
maxval = features[i][j];
maxbin = j;
}
}
if (maxval > chroma_thresh)
q[i] = maxbin;
else
q[i] = feature_length;
}
}
if (1) {
/*****************************/
/* scale all the features to 'balance covariances' during HMM training */
double scale = 10;
for (i = 0; i < frames_read; i++)
for (j = 0; j < feature_length; j++)
features[i][j] *= scale;
/* train an HMM on the features */
/* create a model */
model_t* model = hmm_init(features, frames_read, feature_length, nHMM_states);
/* train the model */
hmm_train(features, frames_read, model);
/*
printf("\n\nafter training:\n");
hmm_print(model);
*/
/* decode the hidden state sequence */
viterbi_decode(features, frames_read, model, q);
hmm_close(model);
/*****************************/
}
/*****************************/
/*
fprintf(stderr, "HMM state sequence:\n");
for (i = 0; i < frames_read; i++)
fprintf(stderr, "%d ", q[i]);
fprintf(stderr, "\n\n");
*/
/* create histograms of states */
double* h = (double*) malloc(frames_read*nHMM_states*sizeof(double)); /* vector in row major order */
create_histograms(q, frames_read, nHMM_states, histogram_length, h);
/* cluster the histograms */
int nbsched = 20; /* length of inverse temperature schedule */
double* bsched = (double*) malloc(nbsched*sizeof(double)); /* inverse temperature schedule */
double b0 = 100;
double alpha = 0.7;
bsched[0] = b0;
for (i = 1; i < nbsched; i++)
bsched[i] = alpha * bsched[i-1];
cluster_melt(h, nHMM_states, frames_read, bsched, nbsched, nclusters, neighbour_limit, q);
/* now q holds a sequence of cluster assignments */
free(h);
free(bsched);
}
/* segment constant-Q or chroma features */
void constq_segment(int* q, double** features, int frames_read, int bins, int ncoeff, int feature_type,
int nHMM_states, int histogram_length, int nclusters, int neighbour_limit)
{
int feature_length;
double** chroma;
int i;
if (feature_type == FEATURE_TYPE_CONSTQ)
{
/* fprintf(stderr, "Converting to dB and normalising...\n");
*/
mpeg7_constq(features, frames_read, ncoeff);
/*
fprintf(stderr, "Running PCA...\n");
*/
/* do PCA on the features (but not the envelope) */
int ncomponents = 20;
pca_project(features, frames_read, ncoeff, ncomponents);
/* copy the envelope so that it immediatly follows the chosen components */
for (i = 0; i < frames_read; i++)
features[i][ncomponents] = features[i][ncoeff];
feature_length = ncomponents + 1;
/**************************************
//TEST
// feature file name
char* dir = "/Users/mark/documents/semma/audio/";
char* file_name = (char*) malloc((strlen(dir) + strlen(trackname) + strlen("_features_c20r8h0.2f0.6.mat") + 1)*sizeof(char));
strcpy(file_name, dir);
strcat(file_name, trackname);
strcat(file_name, "_features_c20r8h0.2f0.6.mat");
// get the features from Matlab from mat-file
int frames_in_file;
readmatarray_size(file_name, 2, &frames_in_file, &feature_length);
readmatarray(file_name, 2, frames_in_file, feature_length, features);
// copy final frame to ensure that we get as many as we expected
int missing_frames = frames_read - frames_in_file;
while (missing_frames > 0)
{
for (i = 0; i < feature_length; i++)
features[frames_read-missing_frames][i] = features[frames_read-missing_frames-1][i];
--missing_frames;
}
free(file_name);
******************************************/
cluster_segment(q, features, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit);
}
if (feature_type == FEATURE_TYPE_CHROMA)
{
/*
fprintf(stderr, "Converting to chroma features...\n");
*/
/* convert constant-Q to normalised chroma features */
chroma = (double**) malloc(frames_read*sizeof(double*));
for (i = 0; i < frames_read; i++)
chroma[i] = (double*) malloc(bins*sizeof(double));
cq2chroma(features, frames_read, ncoeff, bins, chroma);
feature_length = bins;
cluster_segment(q, chroma, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit);
for (i = 0; i < frames_read; i++)
free(chroma[i]);
free(chroma);
}
}