Mercurial > hg > qm-dsp
changeset 278:833ca65b0820
* Update with fixes from Matthew's newer code
| author | Chris Cannam <c.cannam@qmul.ac.uk> |
|---|---|
| date | Mon, 09 Feb 2009 16:05:32 +0000 |
| parents | 09bceb0aeff6 |
| children | c8908cdc8c32 |
| files | dsp/tempotracking/TempoTrackV2.cpp dsp/tempotracking/TempoTrackV2.h |
| diffstat | 2 files changed, 371 insertions(+), 417 deletions(-) [+] |
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--- a/dsp/tempotracking/TempoTrackV2.cpp Tue Jan 20 15:01:01 2009 +0000 +++ b/dsp/tempotracking/TempoTrackV2.cpp Mon Feb 09 16:05:32 2009 +0000 @@ -12,6 +12,7 @@ #include <cmath> #include <cstdlib> +#include <iostream> //#define FRAMESIZE 512 @@ -25,543 +26,494 @@ void TempoTrackV2::adapt_thresh(d_vec_t &df) { + d_vec_t smoothed(df.size()); + + int p_post = 7; + int p_pre = 8; - d_vec_t smoothed(df.size()); - - int p_post = 7; - int p_pre = 8; + int t = std::min(static_cast<int>(df.size()),p_post); // what is smaller, p_post of df size. This is to avoid accessing outside of arrays - int t = std::min(static_cast<int>(df.size()),p_post); // what is smaller, p_post of df size. This is to avoid accessing outside of arrays + // find threshold for first 't' samples, where a full average cannot be computed yet + for (int i = 0;i <= t;i++) + { + int k = std::min((i+p_pre),static_cast<int>(df.size())); + smoothed[i] = mean_array(df,1,k); + } + // find threshold for bulk of samples across a moving average from [i-p_pre,i+p_post] + for (uint i = t+1;i < df.size()-p_post;i++) + { + smoothed[i] = mean_array(df,i-p_pre,i+p_post); + } + // for last few samples calculate threshold, again, not enough samples to do as above + for (uint i = df.size()-p_post;i < df.size();i++) + { + int k = std::max((static_cast<int> (i) -p_post),1); + smoothed[i] = mean_array(df,k,df.size()); + } - // find threshold for first 't' samples, where a full average cannot be computed yet - for (int i = 0;i <= t;i++) - { - int k = std::min((i+p_pre),static_cast<int>(df.size())); - smoothed[i] = mean_array(df,1,k); - } - // find threshold for bulk of samples across a moving average from [i-p_pre,i+p_post] - for (uint i = t+1;i < df.size()-p_post;i++) - { - smoothed[i] = mean_array(df,i-p_pre,i+p_post); - } - // for last few samples calculate threshold, again, not enough samples to do as above - for (uint i = df.size()-p_post;i < df.size();i++) - { - int k = std::max((static_cast<int> (i) -p_post),1); - smoothed[i] = mean_array(df,k,df.size()); - } - - // subtract the threshold from the detection function and check that it is not less than 0 - for (uint i = 0;i < df.size();i++) - { - df[i] -= smoothed[i]; - if (df[i] < 0) - { - df[i] = 0; - } - } + // subtract the threshold from the detection function and check that it is not less than 0 + for (uint i = 0;i < df.size();i++) + { + df[i] -= smoothed[i]; + if (df[i] < 0) + { + df[i] = 0; + } + } } double TempoTrackV2::mean_array(const d_vec_t &dfin,int start,int end) { + double sum = 0.; + + // find sum + for (int i = start;i < end;i++) + { + sum += dfin[i]; + } - double sum = 0.; - - // find sum - for (int i = start;i < end+1;i++) - { - sum += dfin[i]; - } - - return static_cast<double> (sum / (end - start + 1) ); // average and return + return static_cast<double> (sum / (end - start + 1) ); // average and return } void TempoTrackV2::filter_df(d_vec_t &df) { + d_vec_t a(3); + d_vec_t b(3); + d_vec_t lp_df(df.size()); + //equivalent in matlab to [b,a] = butter(2,0.4); + a[0] = 1.0000; + a[1] = -0.3695; + a[2] = 0.1958; + b[0] = 0.2066; + b[1] = 0.4131; + b[2] = 0.2066; + + double inp1 = 0.; + double inp2 = 0.; + double out1 = 0.; + double out2 = 0.; - d_vec_t a(3); - d_vec_t b(3); - d_vec_t lp_df(df.size()); - //equivalent in matlab to [b,a] = butter(2,0.4); - a[0] = 1.0000; - a[1] = -0.3695; - a[2] = 0.1958; - b[0] = 0.2066; - b[1] = 0.4131; - b[2] = 0.2066; - - double inp1 = 0.; - double inp2 = 0.; - double out1 = 0.; - double out2 = 0.; + // forwards filtering + for (uint i = 0;i < df.size();i++) + { + lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2; + inp2 = inp1; + inp1 = df[i]; + out2 = out1; + out1 = lp_df[i]; + } + // copy forwards filtering to df... + // but, time-reversed, ready for backwards filtering + for (uint i = 0;i < df.size();i++) + { + df[i] = lp_df[df.size()-i-1]; + } - // forwards filtering - for (uint i = 0;i < df.size();i++) - { - lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2; - inp2 = inp1; - inp1 = df[i]; - out2 = out1; - out1 = lp_df[i]; - } + for (uint i = 0;i < df.size();i++) + { + lp_df[i] = 0.; + } - - // copy forwards filtering to df... - // but, time-reversed, ready for backwards filtering - for (uint i = 0;i < df.size();i++) - { - df[i] = lp_df[df.size()-i]; - } - - for (uint i = 0;i < df.size();i++) - { - lp_df[i] = 0.; - } - - inp1 = 0.; inp2 = 0.; - out1 = 0.; out2 = 0.; + inp1 = 0.; inp2 = 0.; + out1 = 0.; out2 = 0.; // backwards filetering on time-reversed df - for (uint i = 0;i < df.size();i++) - { - lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2; - inp2 = inp1; - inp1 = df[i]; - out2 = out1; - out1 = lp_df[i]; - } + for (uint i = 0;i < df.size();i++) + { + lp_df[i] = b[0]*df[i] + b[1]*inp1 + b[2]*inp2 - a[1]*out1 - a[2]*out2; + inp2 = inp1; + inp1 = df[i]; + out2 = out1; + out1 = lp_df[i]; + } // write the re-reversed (i.e. forward) version back to df - for (uint i = 0;i < df.size();i++) - { - df[i] = lp_df[df.size()-i]; - } - - + for (uint i = 0;i < df.size();i++) + { + df[i] = lp_df[df.size()-i-1]; + } } void -TempoTrackV2::calculateBeatPeriod(const d_vec_t &df, d_vec_t &beat_period) +TempoTrackV2::calculateBeatPeriod(const d_vec_t &df, d_vec_t &beat_period, + d_vec_t &tempi) { + // to follow matlab.. split into 512 sample frames with a 128 hop size + // calculate the acf, + // then the rcf.. and then stick the rcfs as columns of a matrix + // then call viterbi decoding with weight vector and transition matrix + // and get best path -// to follow matlab.. split into 512 sample frames with a 128 hop size -// calculate the acf, -// then the rcf.. and then stick the rcfs as columns of a matrix -// then call viterbi decoding with weight vector and transition matrix -// and get best path + uint wv_len = 128; + double rayparam = 43.; - uint wv_len = 128; - double rayparam = 43.; - - // make rayleigh weighting curve - d_vec_t wv(wv_len); - for (uint i=0; i<wv.size(); i++) - { - wv[i] = (static_cast<double> (i) / pow(rayparam,2.)) * exp((-1.*pow(-static_cast<double> (i),2.)) / (2.*pow(rayparam,2.))); - } - - - uint winlen = 512; - uint step = 128; - - d_mat_t rcfmat; - int col_counter = -1; - // main loop for beat period calculation - for (uint i=0; i<(df.size()-winlen); i+=step) - { - // get dfframe - d_vec_t dfframe(winlen); - for (uint k=0; k<winlen; k++) + // make rayleigh weighting curve + d_vec_t wv(wv_len); + for (uint i=0; i<wv.size(); i++) { - dfframe[k] = df[i+k]; - } - // get rcf vector for current frame - d_vec_t rcf(wv_len); - get_rcf(dfframe,wv,rcf); - - rcfmat.push_back( d_vec_t() ); // adds a new column - col_counter++; - for (uint j=0; j<rcf.size(); j++) - { - rcfmat[col_counter].push_back( rcf[j] ); + wv[i] = (static_cast<double> (i) / pow(rayparam,2.)) * exp((-1.*pow(-static_cast<double> (i),2.)) / (2.*pow(rayparam,2.))); } - } + // beat tracking frame size (roughly 6 seconds) and hop (1.5 seconds) + uint winlen = 512; + uint step = 128; + + // matrix to store output of comb filter bank, increment column of matrix at each frame + d_mat_t rcfmat; + int col_counter = -1; + + // main loop for beat period calculation + for (uint i=0; i<(df.size()-winlen); i+=step) + { + // get dfframe + d_vec_t dfframe(winlen); + for (uint k=0; k<winlen; k++) + { + dfframe[k] = df[i+k]; + } + // get rcf vector for current frame + d_vec_t rcf(wv_len); + get_rcf(dfframe,wv,rcf); - // now call viterbi decoding function - viterbi_decode(rcfmat,wv,beat_period); - - - + rcfmat.push_back( d_vec_t() ); // adds a new column + col_counter++; + for (uint j=0; j<rcf.size(); j++) + { + rcfmat[col_counter].push_back( rcf[j] ); + } + } + + // now call viterbi decoding function + viterbi_decode(rcfmat,wv,beat_period,tempi); } void TempoTrackV2::get_rcf(const d_vec_t &dfframe_in, const d_vec_t &wv, d_vec_t &rcf) { - // calculate autocorrelation function - // then rcf - // just hard code for now... don't really need separate functions to do this + // calculate autocorrelation function + // then rcf + // just hard code for now... don't really need separate functions to do this - // make acf + // make acf - d_vec_t dfframe(dfframe_in); + d_vec_t dfframe(dfframe_in); - adapt_thresh(dfframe); + adapt_thresh(dfframe); - d_vec_t acf(dfframe.size()); + d_vec_t acf(dfframe.size()); + + for (uint lag=0; lag<dfframe.size(); lag++) + { + double sum = 0.; + double tmp = 0.; - for (uint lag=0; lag<dfframe.size(); lag++) - { + for (uint n=0; n<(dfframe.size()-lag); n++) + { + tmp = dfframe[n] * dfframe[n+lag]; + sum += tmp; + } + acf[lag] = static_cast<double> (sum/ (dfframe.size()-lag)); + } - double sum = 0.; - double tmp = 0.; + // now apply comb filtering + int numelem = 4; + + for (uint i = 2;i < rcf.size();i++) // max beat period + { + for (int a = 1;a <= numelem;a++) // number of comb elements + { + for (int b = 1-a;b <= a-1;b++) // general state using normalisation of comb elements + { + rcf[i-1] += ( acf[(a*i+b)-1]*wv[i-1] ) / (2.*a-1.); // calculate value for comb filter row + } + } + } + + // apply adaptive threshold to rcf + adapt_thresh(rcf); + + double rcfsum =0.; + for (uint i=0; i<rcf.size(); i++) + { + rcf[i] += EPS ; + rcfsum += rcf[i]; + } - for (uint n=0; n<(dfframe.size()-lag); n++) + // normalise rcf to sum to unity + for (uint i=0; i<rcf.size(); i++) { - tmp = dfframe[n] * dfframe[n+lag]; - sum += tmp; + rcf[i] /= (rcfsum + EPS); } - acf[lag] = static_cast<double> (sum/ (dfframe.size()-lag)); - } - - -// for (uint i=0; i<dfframe.size(); i++) -// { -// cout << dfframe[i] << " " << acf[i] << endl; -// } - -// cout << "~~~~~~~~~~~~~~" << endl; - - - - - - // now apply comb filtering - int numelem = 4; - -// for (uint i = 1;i < 118;i++) // max beat period - for (uint i = 2;i < rcf.size();i++) // max beat period - { - for (int a = 1;a <= numelem;a++) // number of comb elements - { - for (int b = 1-a;b <= a-1;b++) // general state using normalisation of comb elements - { - rcf[i-1] += ( acf[(a*i+b)-1]*wv[i-1] ) / (2.*a-1.); // calculate value for comb filter row - } - } - } - - // apply adaptive threshold to rcf - adapt_thresh(rcf); - - double rcfsum =0.; - for (uint i=0; i<rcf.size(); i++) - { - // rcf[i] *= acf[i]; - rcf[i] += EPS ; - rcfsum += rcf[i]; - } - - // normalise rcf to sum to unity - for (uint i=0; i<rcf.size(); i++) - { - rcf[i] /= (rcfsum + EPS); - } - - - } void -TempoTrackV2::viterbi_decode(const d_mat_t &rcfmat, const d_vec_t &wv, d_vec_t &beat_period) +TempoTrackV2::viterbi_decode(const d_mat_t &rcfmat, const d_vec_t &wv, d_vec_t &beat_period, d_vec_t &tempi) { + // following Kevin Murphy's Viterbi decoding to get best path of + // beat periods through rfcmat - // make transition matrix - d_mat_t tmat; - for (uint i=0;i<wv.size();i++) - { - tmat.push_back ( d_vec_t() ); // adds a new column - for (uint j=0; j<wv.size(); j++) - { - tmat[i].push_back(0.); // fill with zeros initially - } - } + // make transition matrix + d_mat_t tmat; + for (uint i=0;i<wv.size();i++) + { + tmat.push_back ( d_vec_t() ); // adds a new column + for (uint j=0; j<wv.size(); j++) + { + tmat[i].push_back(0.); // fill with zeros initially + } + } + + // variance of Gaussians in transition matrix + // formed of Gaussians on diagonal - implies slow tempo change + double sigma = 8.; + // don't want really short beat periods, or really long ones + for (uint i=20;i <wv.size()-20; i++) + { + for (uint j=20; j<wv.size()-20; j++) + { + double mu = static_cast<double>(i); + tmat[i][j] = exp( (-1.*pow((j-mu),2.)) / (2.*pow(sigma,2.)) ); + } + } - double sigma = 8.; - for (uint i=20;i <wv.size()-20; i++) - { - for (uint j=20; j<wv.size()-20; j++) - { - double mu = static_cast<double>(i); - tmat[i][j] = exp( (-1.*pow((j-mu),2.)) / (2.*pow(sigma,2.)) ); - } - } + // parameters for Viterbi decoding... this part is taken from + // Murphy's matlab - d_mat_t delta; - i_mat_t psi; - for (uint i=0;i <rcfmat.size(); i++) - { - delta.push_back( d_vec_t()); - psi.push_back( i_vec_t()); - for (uint j=0; j<rcfmat[i].size(); j++) - { - delta[i].push_back(0.); // fill with zeros initially - psi[i].push_back(0); // fill with zeros initially - } - } + d_mat_t delta; + i_mat_t psi; + for (uint i=0;i <rcfmat.size(); i++) + { + delta.push_back( d_vec_t()); + psi.push_back( i_vec_t()); + for (uint j=0; j<rcfmat[i].size(); j++) + { + delta[i].push_back(0.); // fill with zeros initially + psi[i].push_back(0); // fill with zeros initially + } + } - uint T = delta.size(); - uint Q = delta[0].size(); + uint T = delta.size(); + uint Q = delta[0].size(); - // initialize first column of delta - for (uint j=0; j<Q; j++) - { - delta[0][j] = wv[j] * rcfmat[0][j]; - psi[0][j] = 0; - } - - double deltasum = 0.; - for (uint i=0; i<Q; i++) - { - deltasum += delta[0][i]; - } - for (uint i=0; i<Q; i++) - { - delta[0][i] /= (deltasum + EPS); - } - - - - for (uint t=1; t<T; t++) - { - d_vec_t tmp_vec(Q); - + // initialize first column of delta for (uint j=0; j<Q; j++) { - - for (uint i=0; i<Q; i++) - { - tmp_vec[i] = delta[t-1][i] * tmat[j][i]; - } - - delta[t][j] = get_max_val(tmp_vec); - - psi[t][j] = get_max_ind(tmp_vec); - - delta[t][j] *= rcfmat[t][j]; - - + delta[0][j] = wv[j] * rcfmat[0][j]; + psi[0][j] = 0; } - + double deltasum = 0.; for (uint i=0; i<Q; i++) { - deltasum += delta[t][i]; + deltasum += delta[0][i]; } for (uint i=0; i<Q; i++) { - delta[t][i] /= (deltasum + EPS); + delta[0][i] /= (deltasum + EPS); } + for (uint t=1; t<T; t++) + { + d_vec_t tmp_vec(Q); + for (uint j=0; j<Q; j++) + { + for (uint i=0; i<Q; i++) + { + tmp_vec[i] = delta[t-1][i] * tmat[j][i]; + } + + delta[t][j] = get_max_val(tmp_vec); - } + psi[t][j] = get_max_ind(tmp_vec); + + delta[t][j] *= rcfmat[t][j]; + } + // normalise current delta column + double deltasum = 0.; + for (uint i=0; i<Q; i++) + { + deltasum += delta[t][i]; + } + for (uint i=0; i<Q; i++) + { + delta[t][i] /= (deltasum + EPS); + } + } -// ofstream tmatfile; -// tmatfile.open("/home/matthewd/Desktop/tmat.txt"); + i_vec_t bestpath(T); + d_vec_t tmp_vec(Q); + for (uint i=0; i<Q; i++) + { + tmp_vec[i] = delta[T-1][i]; + } -// for (uint i=0;i <delta.size(); i++) -// { -// for (uint j=0; j<delta[i].size(); j++) -// { -// tmatfile << rcfmat[i][j] << endl; -// } -// } + // find starting point - best beat period for "last" frame + bestpath[T-1] = get_max_ind(tmp_vec); + + // backtrace through index of maximum values in psi + for (uint t=T-2; t>0 ;t--) + { + bestpath[t] = psi[t+1][bestpath[t+1]]; + } -// tmatfile.close(); + // weird but necessary hack -- couldn't get above loop to terminate at t >= 0 + bestpath[0] = psi[1][bestpath[1]]; - i_vec_t bestpath(T); - d_vec_t tmp_vec(Q); - for (uint i=0; i<Q; i++) - { - tmp_vec[i] = delta[T-1][i]; - } + uint lastind = 0; + for (uint i=0; i<T; i++) + { + uint step = 128; + for (uint j=0; j<step; j++) + { + lastind = i*step+j; + beat_period[lastind] = bestpath[i]; + } + } - - bestpath[T-1] = get_max_ind(tmp_vec); - - for (uint t=T-2; t>0 ;t--) - { - bestpath[t] = psi[t+1][bestpath[t+1]]; - } - // very weird hack! - bestpath[0] = psi[1][bestpath[1]]; + //fill in the last values... + for (uint i=lastind; i<beat_period.size(); i++) + { + beat_period[i] = beat_period[lastind]; + } -// for (uint i=0; i<bestpath.size(); i++) -// { -// cout << bestpath[i] << endl; -// } - - - uint lastind = 0; - for (uint i=0; i<T; i++) - { - uint step = 128; - // cout << bestpath[i] << " " << i << endl; - for (uint j=0; j<step; j++) + for (uint i = 0; i < beat_period.size(); i++) { - lastind = i*step+j; - beat_period[lastind] = bestpath[i]; - + tempi.push_back((60.*44100./512.)/beat_period[i]); } - } - - //fill in the last values... - for (uint i=lastind; i<beat_period.size(); i++) - { - beat_period[i] = beat_period[lastind]; - } - - - } double TempoTrackV2::get_max_val(const d_vec_t &df) { - double maxval = 0.; - for (uint i=0; i<df.size(); i++) - { - - if (maxval < df[i]) + double maxval = 0.; + for (uint i=0; i<df.size(); i++) { - maxval = df[i]; + if (maxval < df[i]) + { + maxval = df[i]; + } } - - } - - return maxval; - + return maxval; } int TempoTrackV2::get_max_ind(const d_vec_t &df) { - - double maxval = 0.; - int ind = 0; - for (uint i=0; i<df.size(); i++) - { - if (maxval < df[i]) + double maxval = 0.; + int ind = 0; + for (uint i=0; i<df.size(); i++) { - maxval = df[i]; - ind = i; + if (maxval < df[i]) + { + maxval = df[i]; + ind = i; + } } - - } - - return ind; - + + return ind; } void TempoTrackV2::normalise_vec(d_vec_t &df) { - double sum = 0.; - for (uint i=0; i<df.size(); i++) - { - sum += df[i]; - } - - for (uint i=0; i<df.size(); i++) - { - df[i]/= (sum + EPS); - } - - + double sum = 0.; + for (uint i=0; i<df.size(); i++) + { + sum += df[i]; + } + + for (uint i=0; i<df.size(); i++) + { + df[i]/= (sum + EPS); + } } void TempoTrackV2::calculateBeats(const d_vec_t &df, const d_vec_t &beat_period, d_vec_t &beats) { + d_vec_t cumscore(df.size()); // store cumulative score + i_vec_t backlink(df.size()); // backlink (stores best beat locations at each time instant) + d_vec_t localscore(df.size()); // localscore, for now this is the same as the detection function - d_vec_t cumscore(df.size()); - i_vec_t backlink(df.size()); - d_vec_t localscore(df.size()); - - // WHEN I FIGURE OUT HOW, I'LL WANT TO DO SOME FILTERING ON THIS... - for (uint i=0; i<df.size(); i++) - { - localscore[i] = df[i]; - backlink[i] = -1; - } - - double tightness = 4.; - double alpha = 0.9; - - // main loop - for (uint i=3*beat_period[0]; i<localscore.size(); i++) - { - int prange_min = -2*beat_period[i]; - int prange_max = round(-0.5*beat_period[i]); - - d_vec_t txwt (prange_max - prange_min + 1); - d_vec_t scorecands (txwt.size()); - - for (uint j=0;j<txwt.size();j++) + for (uint i=0; i<df.size(); i++) { - double mu = static_cast<double> (beat_period[i]); - txwt[j] = exp( -0.5*pow(tightness * log((round(2*mu)-j)/mu),2)); - - scorecands[j] = txwt[j] * cumscore[i+prange_min+j]; + localscore[i] = df[i]; + backlink[i] = -1; } - double vv = get_max_val(scorecands); - int xx = get_max_ind(scorecands); + double tightness = 4.; + double alpha = 0.9; - cumscore[i] = alpha*vv + (1.-alpha)*localscore[i]; + // main loop + for (uint i=0; i<localscore.size(); i++) + { + int prange_min = -2*beat_period[i]; + int prange_max = round(-0.5*beat_period[i]); - backlink[i] = i+prange_min+xx; + // transition range + d_vec_t txwt (prange_max - prange_min + 1); + d_vec_t scorecands (txwt.size()); - } + for (uint j=0;j<txwt.size();j++) + { + double mu = static_cast<double> (beat_period[i]); + txwt[j] = exp( -0.5*pow(tightness * log((round(2*mu)-j)/mu),2)); + // IF IN THE ALLOWED RANGE, THEN LOOK AT CUMSCORE[I+PRANGE_MIN+J + // ELSE LEAVE AT DEFAULT VALUE FROM INITIALISATION: D_VEC_T SCORECANDS (TXWT.SIZE()); - d_vec_t tmp_vec; - for (uint i=cumscore.size() - beat_period[beat_period.size()-1] ; i<cumscore.size(); i++) - { - tmp_vec.push_back(cumscore[i]); - } + int cscore_ind = i+prange_min+j; + if (cscore_ind >= 0) + { + scorecands[j] = txwt[j] * cumscore[cscore_ind]; + } + } - int startpoint = get_max_ind(tmp_vec) + cumscore.size() - beat_period[beat_period.size()-1] ; + // find max value and index of maximum value + double vv = get_max_val(scorecands); + int xx = get_max_ind(scorecands); - i_vec_t ibeats; - ibeats.push_back(startpoint); - while (backlink[ibeats.back()] > 3*beat_period[0]) - { - ibeats.push_back(backlink[ibeats.back()]); - } + cumscore[i] = alpha*vv + (1.-alpha)*localscore[i]; + backlink[i] = i+prange_min+xx; + } + + // STARTING POINT, I.E. LAST BEAT.. PICK A STRONG POINT IN cumscore VECTOR + d_vec_t tmp_vec; + for (uint i=cumscore.size() - beat_period[beat_period.size()-1] ; i<cumscore.size(); i++) + { + tmp_vec.push_back(cumscore[i]); + } + + int startpoint = get_max_ind(tmp_vec) + cumscore.size() - beat_period[beat_period.size()-1] ; + + // USE BACKLINK TO GET EACH NEW BEAT (TOWARDS THE BEGINNING OF THE FILE) + // BACKTRACKING FROM THE END TO THE BEGINNING.. MAKING SURE NOT TO GO BEFORE SAMPLE 0 + i_vec_t ibeats; + ibeats.push_back(startpoint); + while (backlink[ibeats.back()] > 0) + { + ibeats.push_back(backlink[ibeats.back()]); + } - - for (uint i=0; i<ibeats.size(); i++) - { - - beats.push_back( static_cast<double>(ibeats[i]) ); - - // cout << ibeats[i] << " " << beats[i] <<endl; - } + // REVERSE SEQUENCE OF IBEATS AND STORE AS BEATS + for (uint i=0; i<ibeats.size(); i++) + { + beats.push_back( static_cast<double>(ibeats[ibeats.size()-i-1]) ); + } }
--- a/dsp/tempotracking/TempoTrackV2.h Tue Jan 20 15:01:01 2009 +0000 +++ b/dsp/tempotracking/TempoTrackV2.h Mon Feb 09 16:05:32 2009 +0000 @@ -23,7 +23,8 @@ ~TempoTrackV2(); void calculateBeatPeriod(const vector<double> &df, - vector<double> &beatPeriod); + vector<double> &beatPeriod, + vector<double> &tempi); void calculateBeats(const vector<double> &df, const vector<double> &beatPeriod, @@ -39,7 +40,8 @@ double mean_array(const d_vec_t &dfin, int start, int end); void filter_df(d_vec_t &df); void get_rcf(const d_vec_t &dfframe, const d_vec_t &wv, d_vec_t &rcf); - void viterbi_decode(const d_mat_t &rcfmat, const d_vec_t &wv, d_vec_t &bp); + void viterbi_decode(const d_mat_t &rcfmat, const d_vec_t &wv, + d_vec_t &bp, d_vec_t &tempi); double get_max_val(const d_vec_t &df); int get_max_ind(const d_vec_t &df); void normalise_vec(d_vec_t &df);