c@277
|
1 /* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
|
c@277
|
2
|
c@277
|
3 /*
|
c@277
|
4 QM DSP Library
|
c@277
|
5
|
c@277
|
6 Centre for Digital Music, Queen Mary, University of London.
|
c@277
|
7 This file copyright 2008-2009 Matthew Davies and QMUL.
|
c@309
|
8
|
c@309
|
9 This program is free software; you can redistribute it and/or
|
c@309
|
10 modify it under the terms of the GNU General Public License as
|
c@309
|
11 published by the Free Software Foundation; either version 2 of the
|
c@309
|
12 License, or (at your option) any later version. See the file
|
c@309
|
13 COPYING included with this distribution for more information.
|
c@277
|
14 */
|
c@277
|
15
|
c@277
|
16 #include "TempoTrackV2.h"
|
c@277
|
17
|
c@277
|
18 #include <cmath>
|
c@277
|
19 #include <cstdlib>
|
c@278
|
20 #include <iostream>
|
c@277
|
21
|
c@279
|
22 #include "maths/MathUtilities.h"
|
c@277
|
23
|
cannam@493
|
24 using std::vector;
|
cannam@493
|
25
|
c@277
|
26 #define EPS 0.0000008 // just some arbitrary small number
|
c@277
|
27
|
cannam@493
|
28 TempoTrackV2::TempoTrackV2(float rate, int increment) :
|
cannam@493
|
29 m_rate(rate), m_increment(increment) {
|
cannam@493
|
30 }
|
cannam@479
|
31
|
c@277
|
32 TempoTrackV2::~TempoTrackV2() { }
|
c@277
|
33
|
c@277
|
34 void
|
c@277
|
35 TempoTrackV2::filter_df(d_vec_t &df)
|
c@277
|
36 {
|
cannam@501
|
37 int df_len = int(df.size());
|
cannam@501
|
38
|
c@278
|
39 d_vec_t a(3);
|
c@278
|
40 d_vec_t b(3);
|
cannam@501
|
41 d_vec_t lp_df(df_len);
|
c@277
|
42
|
c@278
|
43 //equivalent in matlab to [b,a] = butter(2,0.4);
|
c@278
|
44 a[0] = 1.0000;
|
c@278
|
45 a[1] = -0.3695;
|
c@278
|
46 a[2] = 0.1958;
|
c@278
|
47 b[0] = 0.2066;
|
c@278
|
48 b[1] = 0.4131;
|
c@278
|
49 b[2] = 0.2066;
|
luis@327
|
50
|
c@278
|
51 double inp1 = 0.;
|
c@278
|
52 double inp2 = 0.;
|
c@278
|
53 double out1 = 0.;
|
c@278
|
54 double out2 = 0.;
|
c@277
|
55
|
c@277
|
56
|
c@278
|
57 // forwards filtering
|
cannam@501
|
58 for (int i = 0; i < df_len; i++) {
|
c@278
|
59 lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2;
|
c@278
|
60 inp2 = inp1;
|
c@278
|
61 inp1 = df[i];
|
c@278
|
62 out2 = out1;
|
c@278
|
63 out1 = lp_df[i];
|
c@278
|
64 }
|
c@277
|
65
|
c@278
|
66 // copy forwards filtering to df...
|
c@278
|
67 // but, time-reversed, ready for backwards filtering
|
cannam@501
|
68 for (int i = 0; i < df_len; i++) {
|
cannam@501
|
69 df[i] = lp_df[df_len - i - 1];
|
c@278
|
70 }
|
c@277
|
71
|
cannam@501
|
72 for (int i = 0; i < df_len; i++) {
|
luis@327
|
73 lp_df[i] = 0.;
|
c@278
|
74 }
|
c@277
|
75
|
c@278
|
76 inp1 = 0.; inp2 = 0.;
|
c@278
|
77 out1 = 0.; out2 = 0.;
|
c@277
|
78
|
cannam@479
|
79 // backwards filetering on time-reversed df
|
cannam@501
|
80 for (int i = 0; i < df_len; i++) {
|
c@278
|
81 lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2;
|
c@278
|
82 inp2 = inp1;
|
c@278
|
83 inp1 = df[i];
|
c@278
|
84 out2 = out1;
|
c@278
|
85 out1 = lp_df[i];
|
c@278
|
86 }
|
c@277
|
87
|
cannam@479
|
88 // write the re-reversed (i.e. forward) version back to df
|
cannam@501
|
89 for (int i = 0; i < df_len; i++) {
|
cannam@501
|
90 df[i] = lp_df[df_len - i - 1];
|
c@278
|
91 }
|
c@277
|
92 }
|
c@277
|
93
|
c@277
|
94
|
luis@327
|
95 // MEPD 28/11/12
|
luis@327
|
96 // This function now allows for a user to specify an inputtempo (in BPM)
|
luis@327
|
97 // and a flag "constraintempo" which replaces the general rayleigh weighting for periodicities
|
luis@327
|
98 // with a gaussian which is centered around the input tempo
|
luis@327
|
99 // Note, if inputtempo = 120 and constraintempo = false, then functionality is
|
luis@327
|
100 // as it was before
|
c@277
|
101 void
|
c@304
|
102 TempoTrackV2::calculateBeatPeriod(const vector<double> &df,
|
c@304
|
103 vector<double> &beat_period,
|
luis@327
|
104 vector<double> &tempi,
|
luis@327
|
105 double inputtempo, bool constraintempo)
|
c@277
|
106 {
|
c@278
|
107 // to follow matlab.. split into 512 sample frames with a 128 hop size
|
c@278
|
108 // calculate the acf,
|
c@278
|
109 // then the rcf.. and then stick the rcfs as columns of a matrix
|
c@278
|
110 // then call viterbi decoding with weight vector and transition matrix
|
c@278
|
111 // and get best path
|
c@277
|
112
|
cannam@501
|
113 int wv_len = 128;
|
luis@327
|
114
|
luis@327
|
115 // MEPD 28/11/12
|
luis@327
|
116 // the default value of inputtempo in the beat tracking plugin is 120
|
luis@327
|
117 // so if the user specifies a different inputtempo, the rayparam will be updated
|
luis@327
|
118 // accordingly.
|
luis@327
|
119 // note: 60*44100/512 is a magic number
|
luis@327
|
120 // this might (will?) break if a user specifies a different frame rate for the onset detection function
|
luis@327
|
121 double rayparam = (60*44100/512)/inputtempo;
|
luis@327
|
122
|
c@278
|
123 // make rayleigh weighting curve
|
c@278
|
124 d_vec_t wv(wv_len);
|
luis@327
|
125
|
luis@327
|
126 // check whether or not to use rayleigh weighting (if constraintempo is false)
|
luis@327
|
127 // or use gaussian weighting it (constraintempo is true)
|
cannam@479
|
128 if (constraintempo) {
|
cannam@501
|
129 for (int i = 0; i < wv_len; i++) {
|
luis@327
|
130 // MEPD 28/11/12
|
luis@327
|
131 // do a gaussian weighting instead of rayleigh
|
cannam@501
|
132 wv[i] = exp( (-1.*pow((double(i)-rayparam),2.)) / (2.*pow(rayparam/4.,2.)) );
|
luis@327
|
133 }
|
cannam@479
|
134 } else {
|
cannam@501
|
135 for (int i = 0; i < wv_len; i++) {
|
luis@327
|
136 // MEPD 28/11/12
|
luis@327
|
137 // standard rayleigh weighting over periodicities
|
cannam@501
|
138 wv[i] = (double(i) / pow(rayparam,2.)) * exp((-1.*pow(-double(i),2.)) / (2.*pow(rayparam,2.)));
|
luis@327
|
139 }
|
c@277
|
140 }
|
c@277
|
141
|
c@278
|
142 // beat tracking frame size (roughly 6 seconds) and hop (1.5 seconds)
|
cannam@501
|
143 int winlen = 512;
|
cannam@501
|
144 int step = 128;
|
c@278
|
145
|
c@278
|
146 // matrix to store output of comb filter bank, increment column of matrix at each frame
|
c@278
|
147 d_mat_t rcfmat;
|
c@278
|
148 int col_counter = -1;
|
cannam@501
|
149 int df_len = int(df.size());
|
c@278
|
150
|
c@278
|
151 // main loop for beat period calculation
|
cannam@501
|
152 for (int i = 0; i+winlen < df_len; i+=step) {
|
cannam@479
|
153
|
c@278
|
154 // get dfframe
|
c@278
|
155 d_vec_t dfframe(winlen);
|
cannam@501
|
156 for (int k=0; k < winlen; k++) {
|
c@278
|
157 dfframe[k] = df[i+k];
|
c@278
|
158 }
|
c@278
|
159 // get rcf vector for current frame
|
luis@327
|
160 d_vec_t rcf(wv_len);
|
c@278
|
161 get_rcf(dfframe,wv,rcf);
|
luis@327
|
162
|
c@278
|
163 rcfmat.push_back( d_vec_t() ); // adds a new column
|
c@278
|
164 col_counter++;
|
cannam@501
|
165 for (int j = 0; j < wv_len; j++) {
|
c@278
|
166 rcfmat[col_counter].push_back( rcf[j] );
|
c@278
|
167 }
|
c@278
|
168 }
|
luis@327
|
169
|
c@278
|
170 // now call viterbi decoding function
|
c@278
|
171 viterbi_decode(rcfmat,wv,beat_period,tempi);
|
c@277
|
172 }
|
c@277
|
173
|
c@277
|
174
|
c@277
|
175 void
|
c@277
|
176 TempoTrackV2::get_rcf(const d_vec_t &dfframe_in, const d_vec_t &wv, d_vec_t &rcf)
|
c@277
|
177 {
|
c@278
|
178 // calculate autocorrelation function
|
c@278
|
179 // then rcf
|
c@278
|
180 // just hard code for now... don't really need separate functions to do this
|
c@277
|
181
|
c@278
|
182 // make acf
|
c@277
|
183
|
c@278
|
184 d_vec_t dfframe(dfframe_in);
|
c@277
|
185
|
c@279
|
186 MathUtilities::adaptiveThreshold(dfframe);
|
c@277
|
187
|
cannam@501
|
188 int dfframe_len = int(dfframe.size());
|
cannam@501
|
189 int rcf_len = int(rcf.size());
|
cannam@501
|
190
|
cannam@501
|
191 d_vec_t acf(dfframe_len);
|
c@277
|
192
|
cannam@501
|
193 for (int lag = 0; lag < dfframe_len; lag++) {
|
c@278
|
194 double sum = 0.;
|
c@278
|
195 double tmp = 0.;
|
c@277
|
196
|
cannam@501
|
197 for (int n = 0; n < (dfframe_len - lag); n++) {
|
cannam@501
|
198 tmp = dfframe[n] * dfframe[n + lag];
|
c@278
|
199 sum += tmp;
|
c@278
|
200 }
|
cannam@501
|
201 acf[lag] = double(sum/ (dfframe_len - lag));
|
c@278
|
202 }
|
c@277
|
203
|
c@278
|
204 // now apply comb filtering
|
c@278
|
205 int numelem = 4;
|
luis@327
|
206
|
cannam@501
|
207 for (int i = 2; i < rcf_len; i++) { // max beat period
|
cannam@501
|
208 for (int a = 1; a <= numelem; a++) { // number of comb elements
|
cannam@501
|
209 for (int b = 1-a; b <= a-1; b++) { // general state using normalisation of comb elements
|
cannam@479
|
210 rcf[i-1] += ( acf[(a*i+b)-1]*wv[i-1] ) / (2.*a-1.); // calculate value for comb filter row
|
c@278
|
211 }
|
c@278
|
212 }
|
c@278
|
213 }
|
luis@327
|
214
|
c@278
|
215 // apply adaptive threshold to rcf
|
c@279
|
216 MathUtilities::adaptiveThreshold(rcf);
|
luis@327
|
217
|
c@278
|
218 double rcfsum =0.;
|
cannam@501
|
219 for (int i = 0; i < rcf_len; i++) {
|
c@278
|
220 rcf[i] += EPS ;
|
c@278
|
221 rcfsum += rcf[i];
|
c@278
|
222 }
|
c@277
|
223
|
c@278
|
224 // normalise rcf to sum to unity
|
cannam@501
|
225 for (int i = 0; i < rcf_len; i++) {
|
c@278
|
226 rcf[i] /= (rcfsum + EPS);
|
c@277
|
227 }
|
c@277
|
228 }
|
c@277
|
229
|
c@277
|
230 void
|
c@278
|
231 TempoTrackV2::viterbi_decode(const d_mat_t &rcfmat, const d_vec_t &wv, d_vec_t &beat_period, d_vec_t &tempi)
|
c@277
|
232 {
|
c@278
|
233 // following Kevin Murphy's Viterbi decoding to get best path of
|
c@278
|
234 // beat periods through rfcmat
|
cannam@501
|
235
|
cannam@501
|
236 int wv_len = int(wv.size());
|
cannam@501
|
237
|
c@278
|
238 // make transition matrix
|
c@278
|
239 d_mat_t tmat;
|
cannam@501
|
240 for (int i = 0; i < wv_len; i++) {
|
c@278
|
241 tmat.push_back ( d_vec_t() ); // adds a new column
|
cannam@501
|
242 for (int j = 0; j < wv_len; j++) {
|
c@278
|
243 tmat[i].push_back(0.); // fill with zeros initially
|
c@278
|
244 }
|
c@278
|
245 }
|
luis@327
|
246
|
c@278
|
247 // variance of Gaussians in transition matrix
|
c@278
|
248 // formed of Gaussians on diagonal - implies slow tempo change
|
c@278
|
249 double sigma = 8.;
|
c@278
|
250 // don't want really short beat periods, or really long ones
|
cannam@501
|
251 for (int i = 20; i < wv_len - 20; i++) {
|
cannam@501
|
252 for (int j = 20; j < wv_len - 20; j++) {
|
cannam@501
|
253 double mu = double(i);
|
c@278
|
254 tmat[i][j] = exp( (-1.*pow((j-mu),2.)) / (2.*pow(sigma,2.)) );
|
c@278
|
255 }
|
c@278
|
256 }
|
c@277
|
257
|
c@278
|
258 // parameters for Viterbi decoding... this part is taken from
|
c@278
|
259 // Murphy's matlab
|
c@277
|
260
|
c@278
|
261 d_mat_t delta;
|
c@278
|
262 i_mat_t psi;
|
cannam@501
|
263 for (int i = 0; i < int(rcfmat.size()); i++) {
|
cannam@501
|
264 delta.push_back(d_vec_t());
|
cannam@501
|
265 psi.push_back(i_vec_t());
|
cannam@501
|
266 for (int j = 0; j < int(rcfmat[i].size()); j++) {
|
c@278
|
267 delta[i].push_back(0.); // fill with zeros initially
|
c@278
|
268 psi[i].push_back(0); // fill with zeros initially
|
c@278
|
269 }
|
c@278
|
270 }
|
c@277
|
271
|
cannam@501
|
272 int T = delta.size();
|
c@281
|
273
|
c@281
|
274 if (T < 2) return; // can't do anything at all meaningful
|
c@281
|
275
|
cannam@501
|
276 int Q = delta[0].size();
|
c@277
|
277
|
c@278
|
278 // initialize first column of delta
|
cannam@501
|
279 for (int j = 0; j < Q; j++) {
|
c@278
|
280 delta[0][j] = wv[j] * rcfmat[0][j];
|
c@278
|
281 psi[0][j] = 0;
|
c@277
|
282 }
|
luis@327
|
283
|
c@277
|
284 double deltasum = 0.;
|
cannam@501
|
285 for (int i = 0; i < Q; i++) {
|
c@278
|
286 deltasum += delta[0][i];
|
luis@327
|
287 }
|
cannam@501
|
288 for (int i = 0; i < Q; i++) {
|
c@278
|
289 delta[0][i] /= (deltasum + EPS);
|
luis@327
|
290 }
|
c@277
|
291
|
cannam@501
|
292 for (int t=1; t < T; t++)
|
c@278
|
293 {
|
c@278
|
294 d_vec_t tmp_vec(Q);
|
c@277
|
295
|
cannam@501
|
296 for (int j = 0; j < Q; j++) {
|
cannam@501
|
297 for (int i = 0; i < Q; i++) {
|
c@278
|
298 tmp_vec[i] = delta[t-1][i] * tmat[j][i];
|
luis@327
|
299 }
|
luis@327
|
300
|
luis@327
|
301 delta[t][j] = get_max_val(tmp_vec);
|
c@277
|
302
|
c@278
|
303 psi[t][j] = get_max_ind(tmp_vec);
|
luis@327
|
304
|
c@278
|
305 delta[t][j] *= rcfmat[t][j];
|
c@278
|
306 }
|
c@277
|
307
|
c@278
|
308 // normalise current delta column
|
c@278
|
309 double deltasum = 0.;
|
cannam@501
|
310 for (int i = 0; i < Q; i++) {
|
c@278
|
311 deltasum += delta[t][i];
|
luis@327
|
312 }
|
cannam@501
|
313 for (int i = 0; i < Q; i++) {
|
c@278
|
314 delta[t][i] /= (deltasum + EPS);
|
luis@327
|
315 }
|
c@278
|
316 }
|
c@277
|
317
|
c@278
|
318 i_vec_t bestpath(T);
|
c@278
|
319 d_vec_t tmp_vec(Q);
|
cannam@501
|
320 for (int i = 0; i < Q; i++) {
|
c@278
|
321 tmp_vec[i] = delta[T-1][i];
|
c@278
|
322 }
|
c@277
|
323
|
c@278
|
324 // find starting point - best beat period for "last" frame
|
c@278
|
325 bestpath[T-1] = get_max_ind(tmp_vec);
|
luis@327
|
326
|
c@278
|
327 // backtrace through index of maximum values in psi
|
cannam@501
|
328 for (int t=T-2; t>0 ;t--) {
|
c@278
|
329 bestpath[t] = psi[t+1][bestpath[t+1]];
|
c@278
|
330 }
|
c@277
|
331
|
c@278
|
332 // weird but necessary hack -- couldn't get above loop to terminate at t >= 0
|
c@278
|
333 bestpath[0] = psi[1][bestpath[1]];
|
c@277
|
334
|
cannam@501
|
335 int lastind = 0;
|
cannam@501
|
336 for (int i = 0; i < T; i++) {
|
cannam@501
|
337 int step = 128;
|
cannam@501
|
338 for (int j = 0; j < step; j++) {
|
c@278
|
339 lastind = i*step+j;
|
c@278
|
340 beat_period[lastind] = bestpath[i];
|
c@278
|
341 }
|
c@282
|
342 // std::cerr << "bestpath[" << i << "] = " << bestpath[i] << " (used for beat_periods " << i*step << " to " << i*step+step-1 << ")" << std::endl;
|
c@278
|
343 }
|
c@277
|
344
|
cannam@501
|
345 // fill in the last values...
|
cannam@501
|
346 for (int i = lastind; i < int(beat_period.size()); i++) {
|
c@278
|
347 beat_period[i] = beat_period[lastind];
|
c@278
|
348 }
|
c@277
|
349
|
cannam@501
|
350 for (int i = 0; i < int(beat_period.size()); i++) {
|
c@279
|
351 tempi.push_back((60. * m_rate / m_increment)/beat_period[i]);
|
c@277
|
352 }
|
c@277
|
353 }
|
c@277
|
354
|
c@277
|
355 double
|
c@277
|
356 TempoTrackV2::get_max_val(const d_vec_t &df)
|
c@277
|
357 {
|
c@278
|
358 double maxval = 0.;
|
cannam@501
|
359 int df_len = int(df.size());
|
cannam@501
|
360
|
cannam@501
|
361 for (int i = 0; i < df_len; i++) {
|
cannam@479
|
362 if (maxval < df[i]) {
|
c@278
|
363 maxval = df[i];
|
c@278
|
364 }
|
c@277
|
365 }
|
luis@327
|
366
|
c@278
|
367 return maxval;
|
c@277
|
368 }
|
c@277
|
369
|
c@277
|
370 int
|
c@277
|
371 TempoTrackV2::get_max_ind(const d_vec_t &df)
|
c@277
|
372 {
|
c@278
|
373 double maxval = 0.;
|
c@278
|
374 int ind = 0;
|
cannam@501
|
375 int df_len = int(df.size());
|
cannam@501
|
376
|
cannam@501
|
377 for (int i = 0; i < df_len; i++) {
|
cannam@479
|
378 if (maxval < df[i]) {
|
c@278
|
379 maxval = df[i];
|
c@278
|
380 ind = i;
|
c@278
|
381 }
|
c@277
|
382 }
|
luis@327
|
383
|
c@278
|
384 return ind;
|
c@277
|
385 }
|
c@277
|
386
|
c@277
|
387 void
|
c@277
|
388 TempoTrackV2::normalise_vec(d_vec_t &df)
|
c@277
|
389 {
|
c@278
|
390 double sum = 0.;
|
cannam@501
|
391 int df_len = int(df.size());
|
cannam@501
|
392
|
cannam@501
|
393 for (int i = 0; i < df_len; i++) {
|
c@278
|
394 sum += df[i];
|
c@278
|
395 }
|
luis@327
|
396
|
cannam@501
|
397 for (int i = 0; i < df_len; i++) {
|
c@278
|
398 df[i]/= (sum + EPS);
|
c@278
|
399 }
|
c@277
|
400 }
|
c@277
|
401
|
luis@327
|
402 // MEPD 28/11/12
|
luis@327
|
403 // this function has been updated to allow the "alpha" and "tightness" parameters
|
luis@327
|
404 // of the dynamic program to be set by the user
|
luis@327
|
405 // the default value of alpha = 0.9 and tightness = 4
|
c@277
|
406 void
|
c@304
|
407 TempoTrackV2::calculateBeats(const vector<double> &df,
|
c@304
|
408 const vector<double> &beat_period,
|
luis@327
|
409 vector<double> &beats, double alpha, double tightness)
|
c@277
|
410 {
|
c@281
|
411 if (df.empty() || beat_period.empty()) return;
|
c@281
|
412
|
cannam@501
|
413 int df_len = int(df.size());
|
c@277
|
414
|
cannam@501
|
415 d_vec_t cumscore(df_len); // store cumulative score
|
cannam@501
|
416 i_vec_t backlink(df_len); // backlink (stores best beat locations at each time instant)
|
cannam@501
|
417 d_vec_t localscore(df_len); // localscore, for now this is the same as the detection function
|
cannam@501
|
418
|
cannam@501
|
419 for (int i = 0; i < df_len; i++) {
|
c@278
|
420 localscore[i] = df[i];
|
c@278
|
421 backlink[i] = -1;
|
c@277
|
422 }
|
c@277
|
423
|
luis@327
|
424 //double tightness = 4.;
|
luis@327
|
425 //double alpha = 0.9;
|
luis@327
|
426 // MEPD 28/11/12
|
luis@327
|
427 // debug statements that can be removed.
|
c@330
|
428 // std::cerr << "alpha" << alpha << std::endl;
|
c@330
|
429 // std::cerr << "tightness" << tightness << std::endl;
|
c@277
|
430
|
c@278
|
431 // main loop
|
cannam@501
|
432 for (int i = 0; i < df_len; i++) {
|
cannam@479
|
433
|
c@278
|
434 int prange_min = -2*beat_period[i];
|
c@278
|
435 int prange_max = round(-0.5*beat_period[i]);
|
c@277
|
436
|
c@278
|
437 // transition range
|
cannam@501
|
438 int txwt_len = prange_max - prange_min + 1;
|
cannam@501
|
439 d_vec_t txwt (txwt_len);
|
cannam@501
|
440 d_vec_t scorecands (txwt_len);
|
c@277
|
441
|
cannam@501
|
442 for (int j = 0; j < txwt_len; j++) {
|
cannam@479
|
443
|
cannam@501
|
444 double mu = double(beat_period[i]);
|
c@278
|
445 txwt[j] = exp( -0.5*pow(tightness * log((round(2*mu)-j)/mu),2));
|
c@277
|
446
|
c@278
|
447 // IF IN THE ALLOWED RANGE, THEN LOOK AT CUMSCORE[I+PRANGE_MIN+J
|
c@278
|
448 // ELSE LEAVE AT DEFAULT VALUE FROM INITIALISATION: D_VEC_T SCORECANDS (TXWT.SIZE());
|
c@277
|
449
|
cannam@501
|
450 int cscore_ind = i + prange_min + j;
|
cannam@479
|
451 if (cscore_ind >= 0) {
|
c@278
|
452 scorecands[j] = txwt[j] * cumscore[cscore_ind];
|
c@278
|
453 }
|
c@278
|
454 }
|
c@277
|
455
|
c@278
|
456 // find max value and index of maximum value
|
c@278
|
457 double vv = get_max_val(scorecands);
|
c@278
|
458 int xx = get_max_ind(scorecands);
|
c@277
|
459
|
c@278
|
460 cumscore[i] = alpha*vv + (1.-alpha)*localscore[i];
|
c@278
|
461 backlink[i] = i+prange_min+xx;
|
c@280
|
462
|
c@282
|
463 // std::cerr << "backlink[" << i << "] <= " << backlink[i] << std::endl;
|
c@278
|
464 }
|
c@278
|
465
|
c@278
|
466 // STARTING POINT, I.E. LAST BEAT.. PICK A STRONG POINT IN cumscore VECTOR
|
c@278
|
467 d_vec_t tmp_vec;
|
cannam@501
|
468 for (int i = df_len - beat_period[beat_period.size()-1] ; i < df_len; i++) {
|
c@278
|
469 tmp_vec.push_back(cumscore[i]);
|
luis@327
|
470 }
|
c@278
|
471
|
cannam@479
|
472 int startpoint = get_max_ind(tmp_vec) +
|
cannam@501
|
473 df_len - beat_period[beat_period.size()-1] ;
|
c@278
|
474
|
c@281
|
475 // can happen if no results obtained earlier (e.g. input too short)
|
cannam@501
|
476 if (startpoint >= int(backlink.size())) {
|
cannam@501
|
477 startpoint = int(backlink.size()) - 1;
|
cannam@479
|
478 }
|
c@281
|
479
|
c@278
|
480 // USE BACKLINK TO GET EACH NEW BEAT (TOWARDS THE BEGINNING OF THE FILE)
|
c@278
|
481 // BACKTRACKING FROM THE END TO THE BEGINNING.. MAKING SURE NOT TO GO BEFORE SAMPLE 0
|
c@278
|
482 i_vec_t ibeats;
|
c@278
|
483 ibeats.push_back(startpoint);
|
c@282
|
484 // std::cerr << "startpoint = " << startpoint << std::endl;
|
cannam@479
|
485 while (backlink[ibeats.back()] > 0) {
|
c@282
|
486 // std::cerr << "backlink[" << ibeats.back() << "] = " << backlink[ibeats.back()] << std::endl;
|
c@281
|
487 int b = ibeats.back();
|
c@281
|
488 if (backlink[b] == b) break; // shouldn't happen... haha
|
c@281
|
489 ibeats.push_back(backlink[b]);
|
c@278
|
490 }
|
luis@327
|
491
|
c@278
|
492 // REVERSE SEQUENCE OF IBEATS AND STORE AS BEATS
|
cannam@501
|
493 for (int i = 0; i < int(ibeats.size()); i++) {
|
cannam@501
|
494 beats.push_back(double(ibeats[ibeats.size() - i - 1]));
|
c@278
|
495 }
|
c@277
|
496 }
|
c@277
|
497
|
c@277
|
498
|