c@243: /* c@243: * cluster_segmenter.c c@243: * soundbite c@243: * c@243: * Created by Mark Levy on 06/04/2006. c@309: * Copyright 2006 Centre for Digital Music, Queen Mary, University of London. c@309: c@309: This program is free software; you can redistribute it and/or c@309: modify it under the terms of the GNU General Public License as c@309: published by the Free Software Foundation; either version 2 of the c@309: License, or (at your option) any later version. See the file c@309: COPYING included with this distribution for more information. c@243: * c@243: */ c@243: c@243: #include "cluster_segmenter.h" c@243: c@243: extern int readmatarray_size(const char *filepath, int n_array, int* t, int* d); c@243: extern int readmatarray(const char *filepath, int n_array, int t, int d, double** arr); c@243: c@243: /* converts constant-Q features to normalised chroma */ c@243: void cq2chroma(double** cq, int nframes, int ncoeff, int bins, double** chroma) c@243: { c@243: int noct = ncoeff / bins; /* number of complete octaves in constant-Q */ c@243: int t, b, oct, ix; c@243: //double maxchroma; /* max chroma value at each time, for normalisation */ c@243: //double sum; /* for normalisation */ c@243: c@243: for (t = 0; t < nframes; t++) c@243: { c@243: for (b = 0; b < bins; b++) c@243: chroma[t][b] = 0; c@243: for (oct = 0; oct < noct; oct++) c@243: { c@243: ix = oct * bins; c@243: for (b = 0; b < bins; b++) c@243: chroma[t][b] += fabs(cq[t][ix+b]); c@243: } c@243: /* normalise to unit sum c@243: sum = 0; c@243: for (b = 0; b < bins; b++) c@243: sum += chroma[t][b]; c@243: for (b = 0; b < bins; b++) c@243: chroma[t][b] /= sum; c@245: */ c@243: /* normalise to unit max - NO this made results much worse! c@243: maxchroma = 0; c@243: for (b = 0; b < bins; b++) c@243: if (chroma[t][b] > maxchroma) c@243: maxchroma = chroma[t][b]; c@243: if (maxchroma > 0) c@243: for (b = 0; b < bins; b++) c@243: chroma[t][b] /= maxchroma; c@243: */ c@243: } c@243: } c@243: c@243: /* applies MPEG-7 normalisation to constant-Q features, storing normalised envelope (norm) in last feature dimension */ c@243: void mpeg7_constq(double** features, int nframes, int ncoeff) c@243: { c@243: int i, j; c@243: double ss; c@243: double env; c@243: double maxenv = 0; c@243: c@243: /* convert const-Q features to dB scale */ c@243: for (i = 0; i < nframes; i++) c@243: for (j = 0; j < ncoeff; j++) c@243: features[i][j] = 10.0 * log10(features[i][j]+DBL_EPSILON); c@243: c@243: /* normalise each feature vector and add the norm as an extra feature dimension */ c@243: for (i = 0; i < nframes; i++) c@243: { c@243: ss = 0; c@243: for (j = 0; j < ncoeff; j++) c@243: ss += features[i][j] * features[i][j]; c@243: env = sqrt(ss); c@243: for (j = 0; j < ncoeff; j++) c@243: features[i][j] /= env; c@243: features[i][ncoeff] = env; c@243: if (env > maxenv) c@243: maxenv = env; c@243: } c@243: /* normalise the envelopes */ c@243: for (i = 0; i < nframes; i++) c@243: features[i][ncoeff] /= maxenv; c@243: } c@243: c@243: /* return histograms h[nx*m] of data x[nx] into m bins using a sliding window of length h_len (MUST BE ODD) */ c@243: /* NB h is a vector in row major order, as required by cluster_melt() */ c@243: /* for historical reasons we normalise the histograms by their norm (not to sum to one) */ c@243: void create_histograms(int* x, int nx, int m, int hlen, double* h) c@243: { c@243: int i, j, t; c@243: double norm; c@266: c@266: for (i = 0; i < nx*m; i++) c@266: h[i] = 0; c@266: c@243: for (i = hlen/2; i < nx-hlen/2; i++) c@243: { c@243: for (j = 0; j < m; j++) c@243: h[i*m+j] = 0; c@243: for (t = i-hlen/2; t <= i+hlen/2; t++) c@243: ++h[i*m+x[t]]; c@243: norm = 0; c@243: for (j = 0; j < m; j++) c@243: norm += h[i*m+j] * h[i*m+j]; c@243: for (j = 0; j < m; j++) c@243: h[i*m+j] /= norm; c@243: } c@243: c@243: /* duplicate histograms at beginning and end to create one histogram for each data value supplied */ c@243: for (i = 0; i < hlen/2; i++) c@243: for (j = 0; j < m; j++) c@243: h[i*m+j] = h[hlen/2*m+j]; c@243: for (i = nx-hlen/2; i < nx; i++) c@243: for (j = 0; j < m; j++) c@243: h[i*m+j] = h[(nx-hlen/2-1)*m+j]; c@243: } c@243: c@243: /* segment using HMM and then histogram clustering */ c@243: void cluster_segment(int* q, double** features, int frames_read, int feature_length, int nHMM_states, c@243: int histogram_length, int nclusters, int neighbour_limit) c@243: { c@243: int i, j; c@243: c@243: /*****************************/ c@243: if (0) { c@243: /* try just using the predominant bin number as a 'decoded state' */ c@243: nHMM_states = feature_length + 1; /* allow a 'zero' state */ c@243: double chroma_thresh = 0.05; c@243: double maxval; c@243: int maxbin; c@243: for (i = 0; i < frames_read; i++) c@243: { c@243: maxval = 0; c@243: for (j = 0; j < feature_length; j++) c@243: { c@243: if (features[i][j] > maxval) c@243: { c@243: maxval = features[i][j]; c@243: maxbin = j; c@243: } c@243: } c@243: if (maxval > chroma_thresh) c@243: q[i] = maxbin; c@243: else c@243: q[i] = feature_length; c@243: } c@243: c@243: } c@243: if (1) { c@243: /*****************************/ c@243: c@243: c@243: /* scale all the features to 'balance covariances' during HMM training */ c@243: double scale = 10; c@243: for (i = 0; i < frames_read; i++) c@243: for (j = 0; j < feature_length; j++) c@243: features[i][j] *= scale; c@243: c@243: /* train an HMM on the features */ c@243: c@243: /* create a model */ c@243: model_t* model = hmm_init(features, frames_read, feature_length, nHMM_states); c@243: c@243: /* train the model */ c@243: hmm_train(features, frames_read, model); c@283: /* c@243: printf("\n\nafter training:\n"); c@243: hmm_print(model); c@283: */ c@243: /* decode the hidden state sequence */ c@243: viterbi_decode(features, frames_read, model, q); c@243: hmm_close(model); c@243: c@243: /*****************************/ c@243: } c@243: /*****************************/ c@243: c@243: c@283: /* c@243: fprintf(stderr, "HMM state sequence:\n"); c@243: for (i = 0; i < frames_read; i++) c@243: fprintf(stderr, "%d ", q[i]); c@243: fprintf(stderr, "\n\n"); c@283: */ c@243: c@243: /* create histograms of states */ c@243: double* h = (double*) malloc(frames_read*nHMM_states*sizeof(double)); /* vector in row major order */ c@243: create_histograms(q, frames_read, nHMM_states, histogram_length, h); c@243: c@243: /* cluster the histograms */ c@243: int nbsched = 20; /* length of inverse temperature schedule */ c@243: double* bsched = (double*) malloc(nbsched*sizeof(double)); /* inverse temperature schedule */ c@243: double b0 = 100; c@243: double alpha = 0.7; c@243: bsched[0] = b0; c@243: for (i = 1; i < nbsched; i++) c@243: bsched[i] = alpha * bsched[i-1]; c@243: cluster_melt(h, nHMM_states, frames_read, bsched, nbsched, nclusters, neighbour_limit, q); c@243: c@243: /* now q holds a sequence of cluster assignments */ c@243: c@243: free(h); c@243: free(bsched); c@243: } c@243: c@243: /* segment constant-Q or chroma features */ c@243: void constq_segment(int* q, double** features, int frames_read, int bins, int ncoeff, int feature_type, c@243: int nHMM_states, int histogram_length, int nclusters, int neighbour_limit) c@243: { c@243: int feature_length; c@243: double** chroma; c@243: int i; c@243: c@243: if (feature_type == FEATURE_TYPE_CONSTQ) c@243: { c@283: /* fprintf(stderr, "Converting to dB and normalising...\n"); c@283: */ c@243: mpeg7_constq(features, frames_read, ncoeff); c@283: /* c@243: fprintf(stderr, "Running PCA...\n"); c@283: */ c@243: /* do PCA on the features (but not the envelope) */ c@243: int ncomponents = 20; c@243: pca_project(features, frames_read, ncoeff, ncomponents); c@243: c@243: /* copy the envelope so that it immediatly follows the chosen components */ c@243: for (i = 0; i < frames_read; i++) c@243: features[i][ncomponents] = features[i][ncoeff]; c@243: c@243: feature_length = ncomponents + 1; c@243: c@243: /************************************** c@243: //TEST c@243: // feature file name c@243: char* dir = "/Users/mark/documents/semma/audio/"; c@243: char* file_name = (char*) malloc((strlen(dir) + strlen(trackname) + strlen("_features_c20r8h0.2f0.6.mat") + 1)*sizeof(char)); c@243: strcpy(file_name, dir); c@243: strcat(file_name, trackname); c@243: strcat(file_name, "_features_c20r8h0.2f0.6.mat"); c@243: c@243: // get the features from Matlab from mat-file c@243: int frames_in_file; c@243: readmatarray_size(file_name, 2, &frames_in_file, &feature_length); c@243: readmatarray(file_name, 2, frames_in_file, feature_length, features); c@243: // copy final frame to ensure that we get as many as we expected c@243: int missing_frames = frames_read - frames_in_file; c@243: while (missing_frames > 0) c@243: { c@243: for (i = 0; i < feature_length; i++) c@243: features[frames_read-missing_frames][i] = features[frames_read-missing_frames-1][i]; c@243: --missing_frames; c@243: } c@243: c@243: free(file_name); c@243: ******************************************/ c@243: c@243: cluster_segment(q, features, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit); c@243: } c@243: c@243: if (feature_type == FEATURE_TYPE_CHROMA) c@243: { c@283: /* c@243: fprintf(stderr, "Converting to chroma features...\n"); c@283: */ c@243: /* convert constant-Q to normalised chroma features */ c@243: chroma = (double**) malloc(frames_read*sizeof(double*)); c@243: for (i = 0; i < frames_read; i++) c@243: chroma[i] = (double*) malloc(bins*sizeof(double)); c@243: cq2chroma(features, frames_read, ncoeff, bins, chroma); c@243: feature_length = bins; c@243: c@243: cluster_segment(q, chroma, frames_read, feature_length, nHMM_states, histogram_length, nclusters, neighbour_limit); c@243: c@243: for (i = 0; i < frames_read; i++) c@243: free(chroma[i]); c@243: free(chroma); c@243: } c@243: } c@243: c@243: c@243: