adamstark@46: //=======================================================================
adamstark@46: /** @file BTrack.cpp
adamstark@47:  *  @brief BTrack - a real-time beat tracker
adamstark@46:  *  @author Adam Stark
adamstark@46:  *  @copyright Copyright (C) 2008-2014  Queen Mary University of London
adamstark@46:  *
adamstark@46:  * This program is free software: you can redistribute it and/or modify
adamstark@46:  * it under the terms of the GNU General Public License as published by
adamstark@46:  * the Free Software Foundation, either version 3 of the License, or
adamstark@46:  * (at your option) any later version.
adamstark@46:  *
adamstark@46:  * This program is distributed in the hope that it will be useful,
adamstark@46:  * but WITHOUT ANY WARRANTY; without even the implied warranty of
adamstark@46:  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
adamstark@46:  * GNU General Public License for more details.
adamstark@46:  *
adamstark@46:  * You should have received a copy of the GNU General Public License
adamstark@46:  * along with this program.  If not, see <http://www.gnu.org/licenses/>.
adamstark@46:  */
adamstark@46: //=======================================================================
adamstark@46: 
adamstark@46: #include <iostream>
adamstark@46: #include <cmath>
adamstark@46: #include "BTrack.h"
adamstark@46: #include "samplerate.h"
adamstark@46: using namespace std;
adamstark@46: 
adamstark@46: 
adamstark@46: 
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Constructor
adamstark@46: BTrack :: BTrack()
adamstark@46: {	
adamstark@46: 	float rayparam = 43;
adamstark@46: 	float pi = 3.14159265;
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// initialise parameters
adamstark@46: 	tightness = 5;
adamstark@46: 	alpha = 0.9;
adamstark@46: 	tempo = 120;
adamstark@46: 	est_tempo = 120;
adamstark@46: 	p_fact = 60.*44100./512.;
adamstark@46: 	
adamstark@46: 	m0 = 10;
adamstark@46: 	beat = -1;
adamstark@46: 	
adamstark@46: 	playbeat = 0;
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// create rayleigh weighting vector
adamstark@46: 	for (int n = 0;n < 128;n++)
adamstark@46: 	{
adamstark@46: 		wv[n] = ((float) n / pow(rayparam,2)) * exp((-1*pow((float)-n,2)) / (2*pow(rayparam,2)));
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// initialise prev_delta
adamstark@46: 	for (int i = 0;i < 41;i++)
adamstark@46: 	{
adamstark@46: 		prev_delta[i] = 1;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	float t_mu = 41/2;
adamstark@46: 	float m_sig;
adamstark@46: 	float x;
adamstark@46: 	// create tempo transition matrix
adamstark@46: 	m_sig = 41/8;
adamstark@46: 	for (int i = 0;i < 41;i++)
adamstark@46: 	{
adamstark@46: 		for (int j = 0;j < 41;j++)
adamstark@46: 		{
adamstark@46: 			x = j+1;
adamstark@46: 			t_mu = i+1;
adamstark@46: 			t_tmat[i][j] = (1 / (m_sig * sqrt(2*pi))) * exp( (-1*pow((x-t_mu),2)) / (2*pow(m_sig,2)) );
adamstark@46: 		}
adamstark@46: 	}	
adamstark@46: 	
adamstark@46: 	// tempo is not fixed
adamstark@46: 	tempofix = 0;
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Destructor
adamstark@46: BTrack :: ~BTrack()
adamstark@46: {	
adamstark@46: 	
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Initialise with frame size and set all frame sizes accordingly
adamstark@46: void BTrack :: initialise(int fsize)
adamstark@46: {	
adamstark@46: 	framesize = fsize;
adamstark@46: 	dfbuffer_size = (512*512)/fsize;		// calculate df buffer size
adamstark@46: 	
adamstark@46: 	bperiod = round(60/((((float) fsize)/44100)*tempo));
adamstark@46: 	
adamstark@46: 	dfbuffer = new float[dfbuffer_size];	// create df_buffer
adamstark@46: 	cumscore = new float[dfbuffer_size];	// create cumscore
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// initialise df_buffer to zeros
adamstark@46: 	for (int i = 0;i < dfbuffer_size;i++)
adamstark@46: 	{
adamstark@46: 		dfbuffer[i] = 0;
adamstark@46: 		cumscore[i] = 0;
adamstark@46: 		
adamstark@46: 		
adamstark@46: 		if ((i %  ((int) round(bperiod))) == 0)
adamstark@46: 		{
adamstark@46: 			dfbuffer[i] = 1;
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Add new sample to buffer and apply beat tracking
adamstark@46: void BTrack :: process(float df_sample)
adamstark@46: {	 
adamstark@46: 	m0--;
adamstark@46: 	beat--;
adamstark@46: 	playbeat = 0;
adamstark@46: 	
adamstark@46: 	// move all samples back one step
adamstark@46: 	for (int i=0;i < (dfbuffer_size-1);i++)
adamstark@46: 	{
adamstark@46: 		dfbuffer[i] = dfbuffer[i+1];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// add new sample at the end
adamstark@46: 	dfbuffer[dfbuffer_size-1] = df_sample;	
adamstark@46: 	
adamstark@46: 	// update cumulative score
adamstark@46: 	updatecumscore(df_sample);
adamstark@46: 	
adamstark@46: 	// if we are halfway between beats
adamstark@46: 	if (m0 == 0)
adamstark@46: 	{
adamstark@46: 		predictbeat();
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// if we are at a beat
adamstark@46: 	if (beat == 0)
adamstark@46: 	{
adamstark@46: 		playbeat = 1;	// indicate a beat should be output
adamstark@46: 		
adamstark@46: 		// recalculate the tempo
adamstark@46: 		dfconvert();	
adamstark@46: 		calcTempo();
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Set the tempo of the beat tracker
adamstark@46: void BTrack :: settempo(float tempo)
adamstark@46: {	 
adamstark@46: 	
adamstark@46: 	/////////// TEMPO INDICATION RESET //////////////////
adamstark@46: 	
adamstark@46: 	// firstly make sure tempo is between 80 and 160 bpm..
adamstark@46: 	while (tempo > 160)
adamstark@46: 	{
adamstark@46: 		tempo = tempo/2;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	while (tempo < 80)
adamstark@46: 	{
adamstark@46: 		tempo = tempo * 2;
adamstark@46: 	}
adamstark@46: 		
adamstark@46: 	// convert tempo from bpm value to integer index of tempo probability 
adamstark@46: 	int tempo_index = (int) round((tempo - 80)/2);
adamstark@46: 	
adamstark@46: 	// now set previous tempo observations to zero
adamstark@46: 	for (int i=0;i < 41;i++)
adamstark@46: 	{
adamstark@46: 		prev_delta[i] = 0;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// set desired tempo index to 1
adamstark@46: 	prev_delta[tempo_index] = 1;
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	/////////// CUMULATIVE SCORE ARTIFICAL TEMPO UPDATE //////////////////
adamstark@46: 	
adamstark@46: 	// calculate new beat period
adamstark@46: 	int new_bperiod = (int) round(60/((((float) framesize)/44100)*tempo));
adamstark@46: 	
adamstark@46: 	int bcounter = 1;
adamstark@46: 	// initialise df_buffer to zeros
adamstark@46: 	for (int i = (dfbuffer_size-1);i >= 0;i--)
adamstark@46: 	{
adamstark@46: 		if (bcounter == 1)
adamstark@46: 		{
adamstark@46: 			cumscore[i] = 150;
adamstark@46: 			dfbuffer[i] = 150;
adamstark@46: 		}
adamstark@46: 		else
adamstark@46: 		{
adamstark@46: 			cumscore[i] = 10;
adamstark@46: 			dfbuffer[i] = 10;
adamstark@46: 		}
adamstark@46: 		
adamstark@46: 		bcounter++;
adamstark@46: 		
adamstark@46: 		if (bcounter > new_bperiod)
adamstark@46: 		{
adamstark@46: 			bcounter = 1;
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	/////////// INDICATE THAT THIS IS A BEAT //////////////////
adamstark@46: 	
adamstark@46: 	// beat is now
adamstark@46: 	beat = 0;
adamstark@46: 	
adamstark@46: 	// offbeat is half of new beat period away
adamstark@46: 	m0 = (int) round(((float) new_bperiod)/2);
adamstark@46: }
adamstark@46: 
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // fix tempo to roughly around some value
adamstark@46: void BTrack :: fixtempo(float tempo)
adamstark@46: {	
adamstark@46: 	// firstly make sure tempo is between 80 and 160 bpm..
adamstark@46: 	while (tempo > 160)
adamstark@46: 	{
adamstark@46: 		tempo = tempo/2;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	while (tempo < 80)
adamstark@46: 	{
adamstark@46: 		tempo = tempo * 2;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// convert tempo from bpm value to integer index of tempo probability 
adamstark@46: 	int tempo_index = (int) round((tempo - 80)/2);
adamstark@46: 	
adamstark@46: 	// now set previous fixed previous tempo observation values to zero
adamstark@46: 	for (int i=0;i < 41;i++)
adamstark@46: 	{
adamstark@46: 		prev_delta_fix[i] = 0;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// set desired tempo index to 1
adamstark@46: 	prev_delta_fix[tempo_index] = 1;
adamstark@46: 		
adamstark@46: 	// set the tempo fix flag
adamstark@46: 	tempofix = 1;	
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // do not fix the tempo anymore
adamstark@46: void BTrack :: unfixtempo()
adamstark@46: {	
adamstark@46: 	// set the tempo fix flag
adamstark@46: 	tempofix = 0;	
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // Convert detection function from N samples to 512
adamstark@46: void BTrack :: dfconvert()
adamstark@46: {
adamstark@46: 	float output[512];
adamstark@46: 		
adamstark@46: 	double src_ratio = 512.0/((double) dfbuffer_size);
adamstark@46: 	int BUFFER_LEN = dfbuffer_size;
adamstark@46: 	int output_len;
adamstark@46: 	SRC_DATA	src_data ;
adamstark@46: 	
adamstark@46: 	//output_len = (int) floor (((double) BUFFER_LEN) * src_ratio) ;
adamstark@46: 	output_len = 512;
adamstark@46: 	
adamstark@46: 	src_data.data_in = dfbuffer;
adamstark@46: 	src_data.input_frames = BUFFER_LEN;
adamstark@46: 	
adamstark@46: 	src_data.src_ratio = src_ratio;
adamstark@46: 	
adamstark@46: 	src_data.data_out = output;
adamstark@46: 	src_data.output_frames = output_len;
adamstark@46: 	
adamstark@46: 	src_simple (&src_data, SRC_SINC_BEST_QUALITY, 1);
adamstark@46: 			
adamstark@46: 	for (int i = 0;i < output_len;i++)
adamstark@46: 	{
adamstark@46: 		df512[i] = src_data.data_out[i];
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // To calculate the current tempo expressed as the beat period in detection function samples
adamstark@46: void BTrack :: calcTempo()
adamstark@46: {
adamstark@46: 	// adaptive threshold on input
adamstark@46: 	adapt_thresh(df512,512);
adamstark@46: 		
adamstark@46: 	// calculate auto-correlation function of detection function
adamstark@46: 	acf_bal(df512);
adamstark@46: 	
adamstark@46: 	// calculate output of comb filterbank
adamstark@46: 	getrcfoutput();
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// adaptive threshold on rcf
adamstark@46: 	adapt_thresh(rcf,128);
adamstark@46: 
adamstark@46: 	
adamstark@46: 	int t_index;
adamstark@46: 	int t_index2;
adamstark@46: 	// calculate tempo observation vector from bperiod observation vector
adamstark@46: 	for (int i = 0;i < 41;i++)
adamstark@46: 	{
adamstark@46: 		t_index = (int) round(p_fact / ((float) ((2*i)+80)));
adamstark@46: 		t_index2 = (int) round(p_fact / ((float) ((4*i)+160)));
adamstark@46: 
adamstark@46: 		
adamstark@46: 		t_obs[i] = rcf[t_index-1] + rcf[t_index2-1];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	float maxval;
adamstark@46: 	float maxind;
adamstark@46: 	float curval;
adamstark@46: 	
adamstark@46: 	// if tempo is fixed then always use a fixed set of tempi as the previous observation probability function
adamstark@46: 	if (tempofix == 1)
adamstark@46: 	{
adamstark@46: 		for (int k = 0;k < 41;k++)
adamstark@46: 		{
adamstark@46: 			prev_delta[k] = prev_delta_fix[k];
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: 		
adamstark@46: 	for (int j=0;j < 41;j++)
adamstark@46: 	{
adamstark@46: 		maxval = -1;
adamstark@46: 		for (int i = 0;i < 41;i++)
adamstark@46: 		{
adamstark@46: 			curval = prev_delta[i]*t_tmat[i][j];
adamstark@46: 			
adamstark@46: 			if (curval > maxval)
adamstark@46: 			{
adamstark@46: 				maxval = curval;
adamstark@46: 			}
adamstark@46: 		}
adamstark@46: 		
adamstark@46: 		delta[j] = maxval*t_obs[j];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 
adamstark@46: 	normalise(delta,41);
adamstark@46: 	
adamstark@46: 	maxind = -1;
adamstark@46: 	maxval = -1;
adamstark@46: 	
adamstark@46: 	for (int j=0;j < 41;j++)
adamstark@46: 	{
adamstark@46: 		if (delta[j] > maxval)
adamstark@46: 		{
adamstark@46: 			maxval = delta[j];
adamstark@46: 			maxind = j;
adamstark@46: 		}
adamstark@46: 		
adamstark@46: 		prev_delta[j] = delta[j];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	bperiod = round((60.0*44100.0)/(((2*maxind)+80)*((float) framesize)));
adamstark@46: 	
adamstark@46: 	if (bperiod > 0)
adamstark@46: 	{
adamstark@46: 		est_tempo = 60.0/((((float) framesize) / 44100.0)*bperiod);
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	//cout << bperiod << endl;
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // calculates an adaptive threshold which is used to remove low level energy from detection function and emphasise peaks
adamstark@46: void BTrack :: adapt_thresh(float x[],int N)
adamstark@46: {
adamstark@46: 	//int N = 512; // length of df
adamstark@46: 	int i = 0;
adamstark@46: 	int k,t = 0;
adamstark@46: 	float x_thresh[N];
adamstark@46: 	
adamstark@46: 	int p_post = 7;
adamstark@46: 	int p_pre = 8;
adamstark@46: 	
adamstark@46: 	t = min(N,p_post);	// what is smaller, p_post of df size. This is to avoid accessing outside of arrays
adamstark@46: 	
adamstark@46: 	// find threshold for first 't' samples, where a full average cannot be computed yet 
adamstark@46: 	for (i = 0;i <= t;i++)
adamstark@46: 	{	
adamstark@46: 		k = min((i+p_pre),N);
adamstark@46: 		x_thresh[i] = mean_array(x,1,k);
adamstark@46: 	}
adamstark@46: 	// find threshold for bulk of samples across a moving average from [i-p_pre,i+p_post]
adamstark@46: 	for (i = t+1;i < N-p_post;i++)
adamstark@46: 	{
adamstark@46: 		x_thresh[i] = mean_array(x,i-p_pre,i+p_post);
adamstark@46: 	}
adamstark@46: 	// for last few samples calculate threshold, again, not enough samples to do as above
adamstark@46: 	for (i = N-p_post;i < N;i++)
adamstark@46: 	{
adamstark@46: 		k = max((i-p_post),1);
adamstark@46: 		x_thresh[i] = mean_array(x,k,N);
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// subtract the threshold from the detection function and check that it is not less than 0
adamstark@46: 	for (i = 0;i < N;i++)
adamstark@46: 	{
adamstark@46: 		x[i] = x[i] - x_thresh[i];
adamstark@46: 		if (x[i] < 0)
adamstark@46: 		{
adamstark@46: 			x[i] = 0;
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // returns the output of the comb filter
adamstark@46: void BTrack :: getrcfoutput()
adamstark@46: {
adamstark@46: 	int numelem;
adamstark@46: 	
adamstark@46: 	for (int i = 0;i < 128;i++)
adamstark@46: 	{
adamstark@46: 		rcf[i] = 0;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	numelem = 4;
adamstark@46: 	
adamstark@46: 	for (int i = 2;i <= 127;i++) // max beat period
adamstark@46: 	{
adamstark@46: 		for (int a = 1;a <= numelem;a++) // number of comb elements
adamstark@46: 		{
adamstark@46: 			for (int b = 1-a;b <= a-1;b++) // general state using normalisation of comb elements
adamstark@46: 			{
adamstark@46: 				rcf[i-1] = rcf[i-1] + (acf[(a*i+b)-1]*wv[i-1])/(2*a-1);	// calculate value for comb filter row
adamstark@46: 			}
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // calculates the balanced autocorrelation of the smoothed detection function
adamstark@46: void BTrack :: acf_bal(float df_thresh[])
adamstark@46: {
adamstark@46: 	int l, n = 0;
adamstark@46: 	float sum, tmp;
adamstark@46: 	
adamstark@46: 	// for l lags from 0-511
adamstark@46: 	for (l = 0;l < 512;l++)
adamstark@46: 	{
adamstark@46: 		sum = 0;	
adamstark@46: 		
adamstark@46: 		// for n samples from 0 - (512-lag)
adamstark@46: 		for (n = 0;n < (512-l);n++)
adamstark@46: 		{
adamstark@46: 			tmp = df_thresh[n] * df_thresh[n+l];	// multiply current sample n by sample (n+l)
adamstark@46: 			sum = sum + tmp;	// add to sum
adamstark@46: 		}
adamstark@46: 		
adamstark@46: 		acf[l] = sum / (512-l);		// weight by number of mults and add to acf buffer
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // calculates the mean of values in an array from index locations [start,end]
adamstark@46: float BTrack :: mean_array(float array[],int start,int end)
adamstark@46: {
adamstark@46: 	int i;
adamstark@47: 	double sum = 0;
adamstark@47: 
adamstark@47:     int length = end - start;
adamstark@46: 	
adamstark@46: 	// find sum
adamstark@47: 	for (i = start;i < end;i++)
adamstark@46: 	{
adamstark@46: 		sum = sum + array[i];
adamstark@46: 	}
adamstark@46: 	
adamstark@47:     if (length > 0)
adamstark@47:     {
adamstark@47:         return sum / length;	// average and return
adamstark@47:     }
adamstark@47:     else
adamstark@47:     {
adamstark@47:         return 0;
adamstark@47:     }
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // normalise the array
adamstark@46: void BTrack :: normalise(float array[],int N)
adamstark@46: {
adamstark@46: 	double sum = 0;
adamstark@46: 	
adamstark@46: 	for (int i = 0;i < N;i++)
adamstark@46: 	{
adamstark@46: 		if (array[i] > 0)
adamstark@46: 		{
adamstark@46: 			sum = sum + array[i];
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	if (sum > 0)
adamstark@46: 	{
adamstark@46: 		for (int i = 0;i < N;i++)
adamstark@46: 		{
adamstark@46: 			array[i] = array[i] / sum;
adamstark@46: 		}
adamstark@46: 	}
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // plot contents of detection function buffer
adamstark@46: void BTrack :: plotdfbuffer()
adamstark@46: {	 
adamstark@46: 	for (int i=0;i < dfbuffer_size;i++)
adamstark@46: 	{
adamstark@46: 		cout << dfbuffer[i] << endl;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	cout << "--------------------------------" << endl;
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // update the cumulative score
adamstark@46: void BTrack :: updatecumscore(float df_sample)
adamstark@46: {	 
adamstark@46: 	int start, end, winsize;
adamstark@46: 	float max;
adamstark@46: 	
adamstark@46: 	start = dfbuffer_size - round(2*bperiod);
adamstark@46: 	end = dfbuffer_size - round(bperiod/2);
adamstark@46: 	winsize = end-start+1;
adamstark@46: 	
adamstark@46: 	float w1[winsize];
adamstark@46: 	float v = -2*bperiod;
adamstark@46: 	float wcumscore;
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// create window
adamstark@46: 	for (int i = 0;i < winsize;i++)
adamstark@46: 	{
adamstark@46: 		w1[i] = exp((-1*pow(tightness*log(-v/bperiod),2))/2);
adamstark@46: 		v = v+1;
adamstark@46: 	}	
adamstark@46: 	
adamstark@46: 	// calculate new cumulative score value
adamstark@46: 	max = 0;
adamstark@46: 	int n = 0;
adamstark@46: 	for (int i=start;i <= end;i++)
adamstark@46: 	{
adamstark@46: 			wcumscore = cumscore[i]*w1[n];
adamstark@46: 		
adamstark@46: 			if (wcumscore > max)
adamstark@46: 			{
adamstark@46: 				max = wcumscore;
adamstark@46: 			}
adamstark@46: 		n++;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// shift cumulative score back one
adamstark@46: 	for (int i = 0;i < (dfbuffer_size-1);i++)
adamstark@46: 	{
adamstark@46: 		cumscore[i] = cumscore[i+1];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// add new value to cumulative score
adamstark@46: 	cumscore[dfbuffer_size-1] = ((1-alpha)*df_sample) + (alpha*max);
adamstark@46: 	
adamstark@46: 	cscoreval = cumscore[dfbuffer_size-1];
adamstark@46: 	
adamstark@46: 	//cout << cumscore[dfbuffer_size-1] << endl;
adamstark@46: 		
adamstark@46: }
adamstark@46: 
adamstark@46: //-------------------------------------------------------------------------------
adamstark@46: // plot contents of detection function buffer
adamstark@46: void BTrack :: predictbeat()
adamstark@46: {	 
adamstark@46: 	int winsize = (int) bperiod;
adamstark@46: 	float fcumscore[dfbuffer_size + winsize];
adamstark@46: 	float w2[winsize];
adamstark@46: 	// copy cumscore to first part of fcumscore
adamstark@46: 	for (int i = 0;i < dfbuffer_size;i++)
adamstark@46: 	{
adamstark@46: 		fcumscore[i] = cumscore[i];
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// create future window
adamstark@46: 	float v = 1;
adamstark@46: 	for (int i = 0;i < winsize;i++)
adamstark@46: 	{
adamstark@46: 		w2[i] = exp((-1*pow((v - (bperiod/2)),2))   /  (2*pow((bperiod/2) ,2)));
adamstark@46: 		v++;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	// create past window
adamstark@46: 	v = -2*bperiod;
adamstark@46: 	int start = dfbuffer_size - round(2*bperiod);
adamstark@46: 	int end = dfbuffer_size - round(bperiod/2);
adamstark@46: 	int pastwinsize = end-start+1;
adamstark@46: 	float w1[pastwinsize];
adamstark@46: 
adamstark@46: 	for (int i = 0;i < pastwinsize;i++)
adamstark@46: 	{
adamstark@46: 		w1[i] = exp((-1*pow(tightness*log(-v/bperiod),2))/2);
adamstark@46: 		v = v+1;
adamstark@46: 	}
adamstark@46: 
adamstark@46: 	
adamstark@46: 
adamstark@46: 	// calculate future cumulative score
adamstark@46: 	float max;
adamstark@46: 	int n;
adamstark@46: 	float wcumscore;
adamstark@46: 	for (int i = dfbuffer_size;i < (dfbuffer_size+winsize);i++)
adamstark@46: 	{
adamstark@46: 		start = i - round(2*bperiod);
adamstark@46: 		end = i - round(bperiod/2);
adamstark@46: 		
adamstark@46: 		max = 0;
adamstark@46: 		n = 0;
adamstark@46: 		for (int k=start;k <= end;k++)
adamstark@46: 		{
adamstark@46: 			wcumscore = fcumscore[k]*w1[n];
adamstark@46: 			
adamstark@46: 			if (wcumscore > max)
adamstark@46: 			{
adamstark@46: 				max = wcumscore;
adamstark@46: 			}
adamstark@46: 			n++;
adamstark@46: 		}
adamstark@46: 		
adamstark@46: 		fcumscore[i] = max;
adamstark@46: 	}
adamstark@46: 	
adamstark@46: 	
adamstark@46: 	// predict beat
adamstark@46: 	max = 0;
adamstark@46: 	n = 0;
adamstark@46: 	
adamstark@46: 	for (int i = dfbuffer_size;i < (dfbuffer_size+winsize);i++)
adamstark@46: 	{
adamstark@46: 		wcumscore = fcumscore[i]*w2[n];
adamstark@46: 		
adamstark@46: 		if (wcumscore > max)
adamstark@46: 		{
adamstark@46: 			max = wcumscore;
adamstark@46: 			beat = n;
adamstark@46: 		}	
adamstark@46: 		
adamstark@46: 		n++;
adamstark@46: 	}
adamstark@46: 		
adamstark@46: 	
adamstark@46: 	// set beat
adamstark@46: 	beat = beat;
adamstark@46: 	
adamstark@46: 	// set next prediction time
adamstark@46: 	m0 = beat+round(bperiod/2);
adamstark@46: 	
adamstark@46: 
adamstark@46: }