view src/BTrack.cpp @ 52:45231107c9d6

Reformatted comments, removed the OnsetDetectionFunction constructor with no arguments, removed a number of unused variables and made changes to the python module to fix some casting problems and removed some unused variables there also. Still getting the same results, so no overall changes to the algorithm.

//=======================================================================
/** @file BTrack.cpp
 *  @brief BTrack - a real-time beat tracker
 *  @author Adam Stark
 *  @copyright Copyright (C) 2008-2014  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 3 of the License, or
 * (at your option) any later version.
 *
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */
//=======================================================================

#include <cmath>
#include <algorithm>
#include "BTrack.h"
#include "samplerate.h"


//=======================================================================
BTrack :: BTrack()
{	
	float rayparam = 43;
	float pi = 3.14159265;
	
	
	// initialise parameters
	tightness = 5;
	alpha = 0.9;
	tempo = 120;
	est_tempo = 120;
	p_fact = 60.*44100./512.;
	
	m0 = 10;
	beat = -1;
	
	playbeat = 0;
	
	
	
	
	// create rayleigh weighting vector
	for (int n = 0;n < 128;n++)
	{
		wv[n] = ((float) n / pow(rayparam,2)) * exp((-1*pow((float)-n,2)) / (2*pow(rayparam,2)));
	}
	
	// initialise prev_delta
	for (int i = 0;i < 41;i++)
	{
		prev_delta[i] = 1;
	}
	
	float t_mu = 41/2;
	float m_sig;
	float x;
	// create tempo transition matrix
	m_sig = 41/8;
	for (int i = 0;i < 41;i++)
	{
		for (int j = 0;j < 41;j++)
		{
			x = j+1;
			t_mu = i+1;
			t_tmat[i][j] = (1 / (m_sig * sqrt(2*pi))) * exp( (-1*pow((x-t_mu),2)) / (2*pow(m_sig,2)) );
		}
	}	
	
	// tempo is not fixed
	tempofix = 0;
}

//=======================================================================
BTrack :: ~BTrack()
{	
	
}



//=======================================================================
void BTrack :: initialise(int fsize)
{	
	framesize = fsize;
	dfbuffer_size = (512*512)/fsize;		// calculate df buffer size
	
	bperiod = round(60/((((float) fsize)/44100)*tempo));
	
	dfbuffer = new float[dfbuffer_size];	// create df_buffer
	cumscore = new float[dfbuffer_size];	// create cumscore
	
	
	// initialise df_buffer to zeros
	for (int i = 0;i < dfbuffer_size;i++)
	{
		dfbuffer[i] = 0;
		cumscore[i] = 0;
		
		
		if ((i %  ((int) round(bperiod))) == 0)
		{
			dfbuffer[i] = 1;
		}
	}
}

//=======================================================================
void BTrack :: process(float df_sample)
{	 
	m0--;
	beat--;
	playbeat = 0;
	
	// move all samples back one step
	for (int i=0;i < (dfbuffer_size-1);i++)
	{
		dfbuffer[i] = dfbuffer[i+1];
	}
	
	// add new sample at the end
	dfbuffer[dfbuffer_size-1] = df_sample;	
	
	// update cumulative score
	updatecumscore(df_sample);
	
	// if we are halfway between beats
	if (m0 == 0)
	{
		predictbeat();
	}
	
	// if we are at a beat
	if (beat == 0)
	{
		playbeat = 1;	// indicate a beat should be output
		
		// recalculate the tempo
		dfconvert();	
		calcTempo();
	}
}

//=======================================================================
void BTrack :: settempo(float tempo)
{	 
	
	/////////// TEMPO INDICATION RESET //////////////////
	
	// firstly make sure tempo is between 80 and 160 bpm..
	while (tempo > 160)
	{
		tempo = tempo/2;
	}
	
	while (tempo < 80)
	{
		tempo = tempo * 2;
	}
		
	// convert tempo from bpm value to integer index of tempo probability 
	int tempo_index = (int) round((tempo - 80)/2);
	
	// now set previous tempo observations to zero
	for (int i=0;i < 41;i++)
	{
		prev_delta[i] = 0;
	}
	
	// set desired tempo index to 1
	prev_delta[tempo_index] = 1;
	
	
	/////////// CUMULATIVE SCORE ARTIFICAL TEMPO UPDATE //////////////////
	
	// calculate new beat period
	int new_bperiod = (int) round(60/((((float) framesize)/44100)*tempo));
	
	int bcounter = 1;
	// initialise df_buffer to zeros
	for (int i = (dfbuffer_size-1);i >= 0;i--)
	{
		if (bcounter == 1)
		{
			cumscore[i] = 150;
			dfbuffer[i] = 150;
		}
		else
		{
			cumscore[i] = 10;
			dfbuffer[i] = 10;
		}
		
		bcounter++;
		
		if (bcounter > new_bperiod)
		{
			bcounter = 1;
		}
	}
	
	/////////// INDICATE THAT THIS IS A BEAT //////////////////
	
	// beat is now
	beat = 0;
	
	// offbeat is half of new beat period away
	m0 = (int) round(((float) new_bperiod)/2);
}

//=======================================================================
void BTrack :: fixtempo(float tempo)
{	
	// firstly make sure tempo is between 80 and 160 bpm..
	while (tempo > 160)
	{
		tempo = tempo/2;
	}
	
	while (tempo < 80)
	{
		tempo = tempo * 2;
	}
	
	// convert tempo from bpm value to integer index of tempo probability 
	int tempo_index = (int) round((tempo - 80)/2);
	
	// now set previous fixed previous tempo observation values to zero
	for (int i=0;i < 41;i++)
	{
		prev_delta_fix[i] = 0;
	}
	
	// set desired tempo index to 1
	prev_delta_fix[tempo_index] = 1;
		
	// set the tempo fix flag
	tempofix = 1;	
}

//=======================================================================
void BTrack :: unfixtempo()
{	
	// set the tempo fix flag
	tempofix = 0;	
}

//=======================================================================
void BTrack :: dfconvert()
{
	float output[512];
		
	double src_ratio = 512.0/((double) dfbuffer_size);
	int BUFFER_LEN = dfbuffer_size;
	int output_len;
	SRC_DATA	src_data ;
	
	//output_len = (int) floor (((double) BUFFER_LEN) * src_ratio) ;
	output_len = 512;
	
	src_data.data_in = dfbuffer;
	src_data.input_frames = BUFFER_LEN;
	
	src_data.src_ratio = src_ratio;
	
	src_data.data_out = output;
	src_data.output_frames = output_len;
	
	src_simple (&src_data, SRC_SINC_BEST_QUALITY, 1);
			
	for (int i = 0;i < output_len;i++)
	{
		df512[i] = src_data.data_out[i];
	}
}

//=======================================================================
void BTrack :: calcTempo()
{
	// adaptive threshold on input
	adapt_thresh(df512,512);
		
	// calculate auto-correlation function of detection function
	acf_bal(df512);
	
	// calculate output of comb filterbank
	getrcfoutput();
	
	
	// adaptive threshold on rcf
	adapt_thresh(rcf,128);

	
	int t_index;
	int t_index2;
	// calculate tempo observation vector from bperiod observation vector
	for (int i = 0;i < 41;i++)
	{
		t_index = (int) round(p_fact / ((float) ((2*i)+80)));
		t_index2 = (int) round(p_fact / ((float) ((4*i)+160)));

		
		t_obs[i] = rcf[t_index-1] + rcf[t_index2-1];
	}
	
	
	float maxval;
	float maxind;
	float curval;
	
	// if tempo is fixed then always use a fixed set of tempi as the previous observation probability function
	if (tempofix == 1)
	{
		for (int k = 0;k < 41;k++)
		{
			prev_delta[k] = prev_delta_fix[k];
		}
	}
		
	for (int j=0;j < 41;j++)
	{
		maxval = -1;
		for (int i = 0;i < 41;i++)
		{
			curval = prev_delta[i]*t_tmat[i][j];
			
			if (curval > maxval)
			{
				maxval = curval;
			}
		}
		
		delta[j] = maxval*t_obs[j];
	}
	

	normalise(delta,41);
	
	maxind = -1;
	maxval = -1;
	
	for (int j=0;j < 41;j++)
	{
		if (delta[j] > maxval)
		{
			maxval = delta[j];
			maxind = j;
		}
		
		prev_delta[j] = delta[j];
	}
	
	bperiod = round((60.0*44100.0)/(((2*maxind)+80)*((float) framesize)));
	
	if (bperiod > 0)
	{
		est_tempo = 60.0/((((float) framesize) / 44100.0)*bperiod);
	}
	
	//cout << bperiod << endl;
}

//=======================================================================
void BTrack :: adapt_thresh(float x[],int N)
{
	//int N = 512; // length of df
	int i = 0;
	int k,t = 0;
	float x_thresh[N];
	
	int p_post = 7;
	int p_pre = 8;
	
	t = std::min(N,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 (i = 0;i <= t;i++)
	{	
		k = std::min((i+p_pre),N);
		x_thresh[i] = mean_array(x,1,k);
	}
	// find threshold for bulk of samples across a moving average from [i-p_pre,i+p_post]
	for (i = t+1;i < N-p_post;i++)
	{
		x_thresh[i] = mean_array(x,i-p_pre,i+p_post);
	}
	// for last few samples calculate threshold, again, not enough samples to do as above
	for (i = N-p_post;i < N;i++)
	{
		k = std::max((i-p_post),1);
		x_thresh[i] = mean_array(x,k,N);
	}
	
	// subtract the threshold from the detection function and check that it is not less than 0
	for (i = 0;i < N;i++)
	{
		x[i] = x[i] - x_thresh[i];
		if (x[i] < 0)
		{
			x[i] = 0;
		}
	}
}

//=======================================================================
void BTrack :: getrcfoutput()
{
	int numelem;
	
	for (int i = 0;i < 128;i++)
	{
		rcf[i] = 0;
	}
	
	numelem = 4;
	
	for (int i = 2;i <= 127;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] = rcf[i-1] + (acf[(a*i+b)-1]*wv[i-1])/(2*a-1);	// calculate value for comb filter row
			}
		}
	}
}

//=======================================================================
void BTrack :: acf_bal(float df_thresh[])
{
	int l, n = 0;
	float sum, tmp;
	
	// for l lags from 0-511
	for (l = 0;l < 512;l++)
	{
		sum = 0;	
		
		// for n samples from 0 - (512-lag)
		for (n = 0;n < (512-l);n++)
		{
			tmp = df_thresh[n] * df_thresh[n+l];	// multiply current sample n by sample (n+l)
			sum = sum + tmp;	// add to sum
		}
		
		acf[l] = sum / (512-l);		// weight by number of mults and add to acf buffer
	}
}

//=======================================================================
float BTrack :: mean_array(float array[],int start,int end)
{
	int i;
	double sum = 0;

    int length = end - start;
	
	// find sum
	for (i = start;i < end;i++)
	{
		sum = sum + array[i];
	}
	
    if (length > 0)
    {
        return sum / length;	// average and return
    }
    else
    {
        return 0;
    }
}

//=======================================================================
void BTrack :: normalise(float array[],int N)
{
	double sum = 0;
	
	for (int i = 0;i < N;i++)
	{
		if (array[i] > 0)
		{
			sum = sum + array[i];
		}
	}
	
	if (sum > 0)
	{
		for (int i = 0;i < N;i++)
		{
			array[i] = array[i] / sum;
		}
	}
}

//=======================================================================
void BTrack :: updatecumscore(float df_sample)
{	 
	int start, end, winsize;
	float max;
	
	start = dfbuffer_size - round(2*bperiod);
	end = dfbuffer_size - round(bperiod/2);
	winsize = end-start+1;
	
	float w1[winsize];
	float v = -2*bperiod;
	float wcumscore;
	
	
	// create window
	for (int i = 0;i < winsize;i++)
	{
		w1[i] = exp((-1*pow(tightness*log(-v/bperiod),2))/2);
		v = v+1;
	}	
	
	// calculate new cumulative score value
	max = 0;
	int n = 0;
	for (int i=start;i <= end;i++)
	{
			wcumscore = cumscore[i]*w1[n];
		
			if (wcumscore > max)
			{
				max = wcumscore;
			}
		n++;
	}
	
	
	// shift cumulative score back one
	for (int i = 0;i < (dfbuffer_size-1);i++)
	{
		cumscore[i] = cumscore[i+1];
	}
	
	// add new value to cumulative score
	cumscore[dfbuffer_size-1] = ((1-alpha)*df_sample) + (alpha*max);
	
	cscoreval = cumscore[dfbuffer_size-1];
	
	//cout << cumscore[dfbuffer_size-1] << endl;
		
}

//=======================================================================
void BTrack :: predictbeat()
{	 
	int winsize = (int) bperiod;
	float fcumscore[dfbuffer_size + winsize];
	float w2[winsize];
	// copy cumscore to first part of fcumscore
	for (int i = 0;i < dfbuffer_size;i++)
	{
		fcumscore[i] = cumscore[i];
	}
	
	// create future window
	float v = 1;
	for (int i = 0;i < winsize;i++)
	{
		w2[i] = exp((-1*pow((v - (bperiod/2)),2))   /  (2*pow((bperiod/2) ,2)));
		v++;
	}
	
	// create past window
	v = -2*bperiod;
	int start = dfbuffer_size - round(2*bperiod);
	int end = dfbuffer_size - round(bperiod/2);
	int pastwinsize = end-start+1;
	float w1[pastwinsize];

	for (int i = 0;i < pastwinsize;i++)
	{
		w1[i] = exp((-1*pow(tightness*log(-v/bperiod),2))/2);
		v = v+1;
	}

	

	// calculate future cumulative score
	float max;
	int n;
	float wcumscore;
	for (int i = dfbuffer_size;i < (dfbuffer_size+winsize);i++)
	{
		start = i - round(2*bperiod);
		end = i - round(bperiod/2);
		
		max = 0;
		n = 0;
		for (int k=start;k <= end;k++)
		{
			wcumscore = fcumscore[k]*w1[n];
			
			if (wcumscore > max)
			{
				max = wcumscore;
			}
			n++;
		}
		
		fcumscore[i] = max;
	}
	
	
	// predict beat
	max = 0;
	n = 0;
	
	for (int i = dfbuffer_size;i < (dfbuffer_size+winsize);i++)
	{
		wcumscore = fcumscore[i]*w2[n];
		
		if (wcumscore > max)
		{
			max = wcumscore;
			beat = n;
		}	
		
		n++;
	}
		
	
	// set beat
	//beat = beat;
	
	// set next prediction time
	m0 = beat+round(bperiod/2);
	

}