Mercurial > hg > bayesian-drum-tracker

changeset 6:fc095148e2a8

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sending out the KL information that cognitive dynamic paper will be using
author Andrew N Robertson <andrew.robertson@eecs.qmul.ac.uk>
date Tue, 06 Mar 2012 17:04:16 +0000
parents 4288150c257b
children 3d30a79c0312
files newOFsrc/BayesDrumTracker.cpp newOFsrc/bayesianArray.cpp newOFsrc/bayesianArray.h newOFsrc/testApp.cpp newOFsrc/testApp.h
diffstat 5 files changed, 120 insertions(+), 102 deletions(-) [+]
line wrap: on
line diff
--- a/newOFsrc/BayesDrumTracker.cpp	Tue Mar 06 14:15:35 2012 +0000
+++ b/newOFsrc/BayesDrumTracker.cpp	Tue Mar 06 17:04:16 2012 +0000
@@ -304,7 +304,7 @@
 	m.addFloatArg(maxPhase);
 	
 	beatTimes.tatum = maxTempo;
-	printf("BEAT INFO %f, %f\n", beatTimes.tatum, maxPhase);
+//	printf("BEAT INFO %f, %f\n", beatTimes.tatum, maxPhase);
 	
 	sender.sendMessage( m );
 	
@@ -320,7 +320,7 @@
 	maxTempo += tempoMinimum;
 	
 	beatTimes.tatum = maxTempo;
-	printf("SEND TATUM %f\n", beatTimes.tatum);
+//	printf("SEND TATUM %f\n", beatTimes.tatum);
 	
 	m.addFloatArg( maxTempo );
 	sender.sendMessage( m );
--- a/newOFsrc/bayesianArray.cpp	Tue Mar 06 14:15:35 2012 +0000
+++ b/newOFsrc/bayesianArray.cpp	Tue Mar 06 17:04:16 2012 +0000
@@ -13,16 +13,16 @@
 
 bayesianArray::bayesianArray(){
 	likelihoodNoise = 0.5;
-	likelihoodMean = ARRAY_SIZE/2;
-	likelihoodStdDev = ARRAY_SIZE / 12;
+	likelihoodMean = arraySize/2;
+	likelihoodStdDev = arraySize / 12;
 initialiseArray();
 }
 
 void bayesianArray::initialiseArray(){
 
 	//maximumIndex = 12;//change this
-	setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);	
-	setGaussianLikelihood(ARRAY_SIZE/2, ARRAY_SIZE/1);//likelihoodMean, likelihoodStdDev);
+	setGaussianPrior(arraySize/2, arraySize/1);	
+	setGaussianLikelihood(arraySize/2, arraySize/1);//likelihoodMean, likelihoodStdDev);
 	
 	calculatePosterior();
 	renormalisePosterior();
@@ -32,9 +32,9 @@
 	earlySixteenthNoteProportion = 0;
 	lateSixteenthNoteProportion = 0;
 	decayNoiseAmount = 0.1;
-	decayNoiseStdDev = ARRAY_SIZE/24;
+	decayNoiseStdDev = arraySize/24;
 	standardDeviation = likelihoodStdDev;
-	setDecayNoiseGaussian(ARRAY_SIZE/2, decayNoiseStdDev);
+	setDecayNoiseGaussian(arraySize/2, decayNoiseStdDev);
 	
 	setGaussianLikelihood(likelihoodMean, likelihoodStdDev);
 }
@@ -42,7 +42,7 @@
 
 void bayesianArray::setGaussianPrior(float mean, float StdDev){
 int i;
-	for (i=0;i<ARRAY_SIZE;i++){
+	for (i=0;i<arraySize;i++){
 	prior[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
 	//posterior[i] = prior[i];
 	}
@@ -50,7 +50,7 @@
 
 void bayesianArray::setGaussianPosterior(float mean, float StdDev){
 int i;
-	for (i=0;i<ARRAY_SIZE;i++){
+	for (i=0;i<arraySize;i++){
 	posterior[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
 	}
 }
@@ -58,72 +58,72 @@
 void bayesianArray::setGaussianLikelihoodForBeats(float mean, float StdDev){
 	//this has eighth and sixteenth positions included
 	
-if (mean >= 0 && mean <= ARRAY_SIZE){
+if (mean >= 0 && mean <= arraySize){
 int i;	float eighthDifference;
 	//main beat position plus these three:
-int eighthPosition = ((int)mean + ARRAY_SIZE/2)%ARRAY_SIZE;
-int earlySixteenthPosition = ((int)mean + (3*ARRAY_SIZE/4))%ARRAY_SIZE;;
-int lateSixteenthPosition = ((int)mean + (ARRAY_SIZE/4))%ARRAY_SIZE;;
+int eighthPosition = ((int)mean + arraySize/2)%arraySize;
+int earlySixteenthPosition = ((int)mean + (3*arraySize/4))%arraySize;;
+int lateSixteenthPosition = ((int)mean + (arraySize/4))%arraySize;;
 
 float mainDifference, sixteenthDifference;
 float gaussianProportion = 1 - likelihoodNoise;
 float mainProportion = (1 - eighthNoteProportion - earlySixteenthNoteProportion - lateSixteenthNoteProportion);
 
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 	
-	mainDifference = min( fabs(i-mean) , (double)(i + ARRAY_SIZE - mean));
+	mainDifference = min( fabs(i-mean) , (double)(i + arraySize - mean));
 	likelihood[i] = gaussianProportion * mainProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(mainDifference)*(mainDifference)/(2*StdDev*StdDev)) ;
 	
-	eighthDifference = min( abs(i - eighthPosition) , i + ARRAY_SIZE - eighthPosition);
-	eighthDifference = min(eighthDifference , (float)(ARRAY_SIZE + eighthPosition - i ));
+	eighthDifference = min( abs(i - eighthPosition) , i + arraySize - eighthPosition);
+	eighthDifference = min(eighthDifference , (float)(arraySize + eighthPosition - i ));
 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
 	likelihood[i] += gaussianProportion * eighthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(eighthDifference)*(eighthDifference)/(2*StdDev*StdDev)) ;
 	
-	sixteenthDifference = min( abs(i - earlySixteenthPosition) , i + ARRAY_SIZE - earlySixteenthPosition);
-	sixteenthDifference = min(sixteenthDifference , (float)(ARRAY_SIZE + earlySixteenthPosition - i ));
+	sixteenthDifference = min( abs(i - earlySixteenthPosition) , i + arraySize - earlySixteenthPosition);
+	sixteenthDifference = min(sixteenthDifference , (float)(arraySize + earlySixteenthPosition - i ));
 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
 	likelihood[i] += gaussianProportion * earlySixteenthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(sixteenthDifference)*(sixteenthDifference)/(2*StdDev*StdDev)) ;
 	
-	sixteenthDifference = min( abs(i - lateSixteenthPosition) , i + ARRAY_SIZE - lateSixteenthPosition);
-	sixteenthDifference = min(sixteenthDifference , (float)(ARRAY_SIZE + lateSixteenthPosition - i ));
+	sixteenthDifference = min( abs(i - lateSixteenthPosition) , i + arraySize - lateSixteenthPosition);
+	sixteenthDifference = min(sixteenthDifference , (float)(arraySize + lateSixteenthPosition - i ));
 	//for e.g. +0.43, or -0.47 we require the gaussian around the half note too
 	likelihood[i] += gaussianProportion * lateSixteenthNoteProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(sixteenthDifference)*(sixteenthDifference)/(2*StdDev*StdDev)) ;
 	
 
 
-	likelihood[i] += (likelihoodNoise / ARRAY_SIZE);
+	likelihood[i] += (likelihoodNoise / arraySize);
 	//likelihood[i] = (float) max(gaussianProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev)) , 
-	//(double) (likelihoodNoise / ARRAY_SIZE) );
+	//(double) (likelihoodNoise / arraySize) );
 	}
-//	renormaliseArray(&likelihood[0], ARRAY_SIZE);
+//	renormaliseArray(&likelihood[0], arraySize);
 }//end if mean within limits
 }
 
 
 void bayesianArray::setGaussianLikelihood(float mean, float StdDev){
-if (mean >= 0 && mean <= ARRAY_SIZE){
+if (mean >= 0 && mean <= arraySize){
 int i;	float eighthDifference;
-int eighthPosition = ((int)mean + ARRAY_SIZE/2)%ARRAY_SIZE;
+int eighthPosition = ((int)mean + arraySize/2)%arraySize;
 float mainDifference;
 float gaussianProportion = 1 - likelihoodNoise;
 
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 	
-	mainDifference = min( fabs(i-mean) , (double)(i + ARRAY_SIZE - mean));
+	mainDifference = min( fabs(i-mean) , (double)(i + arraySize - mean));
 	//without * (1 - eighthNoteProportion)
 	likelihood[i] = gaussianProportion  * (1/(StdDev*sqrt(2*PI)))*exp(-1*(mainDifference)*(mainDifference)/(2*StdDev*StdDev)) ;
 		
-	likelihood[i] += (likelihoodNoise / ARRAY_SIZE);
+	likelihood[i] += (likelihoodNoise / arraySize);
 	//likelihood[i] = (float) max(gaussianProportion * (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev)) , 
-	//(double) (likelihoodNoise / ARRAY_SIZE) );
+	//(double) (likelihoodNoise / arraySize) );
 	}
-//	renormaliseArray(&likelihood[0], ARRAY_SIZE);
+//	renormaliseArray(&likelihood[0], arraySize);
 }//end if mean within limits
 }
 
 void bayesianArray::calculatePosterior(){
 int i;
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 	posterior[i] = likelihood[i] * prior[i];
 	}
 	//renormalisePosterior();
@@ -165,7 +165,7 @@
 	float integratedQuantity = 0;
 	float integratedTotal = 0;
 	double integratedIndex = 0;
-	for (i=0;i < ARRAY_SIZE;i++){	
+	for (i=0;i < arraySize;i++){	
 		integratedQuantity += posterior[i];//the values of the probability distribution
 		integratedTotal += i*posterior[i];
 	}
@@ -182,7 +182,7 @@
 	double total = 0;
 	double pdfSum;
 	double variance = 0;
-	for (int i=0;i < ARRAY_SIZE;i++){	
+	for (int i=0;i < arraySize;i++){	
 //*posterior[i] * 		
 		total += posterior[i] * (i - integratedEstimate) * (i - integratedEstimate);//the values of the probability distribution
 		pdfSum += posterior[i];
@@ -191,7 +191,7 @@
 	if (pdfSum > 0)
 	variance = total / pdfSum;
 	else 
-	variance = ARRAY_SIZE;
+	variance = arraySize;
 
 	standardDeviation = sqrt(variance);
 	return standardDeviation;
@@ -214,7 +214,7 @@
 
 void bayesianArray::resetPrior(){
 	int i;
-	for (i=0;i<ARRAY_SIZE;i++){
+	for (i=0;i<arraySize;i++){
 	prior[i] = posterior[i];
 	}
 }
@@ -222,11 +222,11 @@
 void bayesianArray::renormalisePrior(){
 	int i;
 	float totalArea = 0;
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 		totalArea += prior[i];
 	}
 	
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 		prior[i] /= totalArea;
 	}
 }
@@ -234,22 +234,22 @@
 void bayesianArray::renormalisePosterior(){
 int i;
 float totalArea = 0;
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 		totalArea += posterior[i];
 	}
 	
-	for (i=0;i < ARRAY_SIZE;i++){
+	for (i=0;i < arraySize;i++){
 		posterior[i] /= totalArea;
 	}
 }
 
 void bayesianArray::decayPosterior(){
 	float *pointer;
-	pointer = getMaximumEstimate(&posterior[0], ARRAY_SIZE);	
+	pointer = getMaximumEstimate(&posterior[0], arraySize);	
 	 float maximum;
 	 maximum = *pointer;
 	int i;
-	for (i=0;i<ARRAY_SIZE;i++){
+	for (i=0;i<arraySize;i++){
 	posterior[i] += (maximum - posterior[i]) * posteriorDecayRate * 0.01;;//usded to be * maximum not minus value
 	}
 	maximumIndex = *(pointer+1);
@@ -257,7 +257,7 @@
 
 void bayesianArray::setDecayNoiseGaussian(float mean, float StdDev){
 int i;
-	for (i=0;i<ARRAY_SIZE;i++){
+	for (i=0;i<arraySize;i++){
 	decayNoiseArray[i] = (1/(StdDev*sqrt(2*PI)))*exp(-1*(i-mean)*(i-mean)/(2*StdDev*StdDev));
 	}
 }
@@ -265,9 +265,9 @@
 void bayesianArray::decayPosteriorWithGaussianNoise(){
 
 	int i;
-	float currentMaximum = getMaximum(&posterior[0], ARRAY_SIZE);
-	for (i=0;i<ARRAY_SIZE;i++){
-	posterior[i] += decayNoiseArray[(i - (int)maximumIndex + ((3*ARRAY_SIZE)/2)) % ARRAY_SIZE] * currentMaximum * decayNoiseAmount;
+	float currentMaximum = getMaximum(&posterior[0], arraySize);
+	for (i=0;i<arraySize;i++){
+	posterior[i] += decayNoiseArray[(i - (int)maximumIndex + ((3*arraySize)/2)) % arraySize] * currentMaximum * decayNoiseAmount;
 	//posteriorDecayRate * 0.01;;//usded to be * maximum not minus value
 	}
 
@@ -276,7 +276,7 @@
 void bayesianArray::resetMaximumPosterior(){
 int i;
 float max = 0;
-	for (i=0;i < ARRAY_SIZE;i++){	
+	for (i=0;i < arraySize;i++){	
 		if (posterior[i]>max){
 		maximumIndex = i;
 		max = posterior[i];
@@ -288,12 +288,12 @@
 int tmpIndex;
 		//copy array
 		int i;
-		for (i=0;i < ARRAY_SIZE;i++){
+		for (i=0;i < arraySize;i++){
 		tempPosteriorArray[i] =	posterior[i] ;
 		}
 		//translate values
-	for (i=0;i < ARRAY_SIZE;i++){
-	tmpIndex = (i + translationIndex + ARRAY_SIZE)%ARRAY_SIZE;
+	for (i=0;i < arraySize;i++){
+	tmpIndex = (i + translationIndex + arraySize)%arraySize;
 	posterior[tmpIndex] = tempPosteriorArray[i]; 
 	}
 	//now delete tmp array
@@ -304,7 +304,7 @@
 	//take no chances - renormalise both prior and posterior
 	renormalisePosterior();
 	renormalisePrior();
-	for (int i = 0;i < ARRAY_SIZE;i++){
+	for (int i = 0;i < arraySize;i++){
 		if (posterior[i] > 0 && prior[i] > 0){
 			KLsum += (posterior[i]*log(posterior[i]/prior[i]));
 		}
@@ -315,7 +315,7 @@
 double bayesianArray::getEntropyOfPosterior(){
 	double entropy = 0;
 	//make sure normalised? (it is)
-	for (int i = 0;i < ARRAY_SIZE;i++){
+	for (int i = 0;i < arraySize;i++){
 		if (posterior[i] > 0){
 			entropy += posterior[i]*log(posterior[i]);
 		}
--- a/newOFsrc/bayesianArray.h	Tue Mar 06 14:15:35 2012 +0000
+++ b/newOFsrc/bayesianArray.h	Tue Mar 06 17:04:16 2012 +0000
@@ -10,7 +10,7 @@
 #ifndef	_BAYESIAN_ARRAY
 #define _BAYESIAN_ARRAY
 
-#define ARRAY_SIZE 240
+//#define arraySize 240
 
 
 class bayesianArray{
@@ -42,12 +42,14 @@
 		float* getMaximumEstimate(float *ptr, int length);
 		double getIntegratedEstimateIndex();
 	
-		float prior [ARRAY_SIZE];
-		float posterior [ARRAY_SIZE];
-		float likelihood [ARRAY_SIZE];	
-		float tempPosteriorArray[ARRAY_SIZE];
+	static int const arraySize = 240; 
+	
+		float prior [arraySize];
+		float posterior [arraySize];
+		float likelihood [arraySize];	
+		float tempPosteriorArray[arraySize];
 
-		float decayNoiseArray[ARRAY_SIZE];
+		float decayNoiseArray[arraySize];
 		float decayNoiseStdDev, decayNoiseAmount;
 
 		float likelihoodMean, likelihoodStdDev, likelihoodNoise;
--- a/newOFsrc/testApp.cpp	Tue Mar 06 14:15:35 2012 +0000
+++ b/newOFsrc/testApp.cpp	Tue Mar 06 17:04:16 2012 +0000
@@ -55,29 +55,7 @@
 
 	KLdiv = 0;
 	entropy = 0;
-/*
-	beatDistribution.initialiseArray();
-	tempoDistribution.initialiseArray();
-	beatTimes.lastBeatTime = 0;
-	correctionFactor = 0.5;
 
-
-	
-	tempoDistribution.likelihoodStdDev =  ARRAY_SIZE / 32;
-//	tempoDistribution.likelihoodNoise = 0.96;
-	tempoDistribution.likelihoodNoise = 0.7;	
-	tempoDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);//wide 
-	
-	beatDistribution.likelihoodStdDev = ARRAY_SIZE  / 32;
-	beatDistribution.likelihoodNoise = 0.56;	
-	beatDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);
-
-	
-	tempoMinimum = 180;
-	tempoMaximum = 400;
-	posteriorMaximum = 0.1;
- 
- */
 	hidePriorMode = false;
 
 	printInterval = true;
@@ -106,27 +84,7 @@
 
 
 void testApp::resetParameters(){
-	/*
-	beatTimes.startIndex = 0;
-	beatTimes.lastBeatTime = 0;
-	maxPhase = 0;
-	posteriorMaximum = 0.1;
-	
-	accompanimentStarted = false;
 
-	tempoDistribution.likelihoodNoise = 0.8;	
-	tempoDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/2);//wide 
-	
-	beatDistribution.initialiseArray();
-	tempoDistribution.initialiseArray();
-	
-	tempoDistribution.calculateStandardDeviation();
-	beatDistribution.calculateStandardDeviation();
-	
-	tempoStdDev = tempoDistribution.standardDeviation;
-	
-	beatTimes.resetBeatTimeArray();
-	*/
 }
 
 //--------------------------------------------------------------
@@ -168,11 +126,22 @@
 		if ( m.getAddress() == "/beatError" ){
 			double timeNow = ofGetElapsedTimeMillis();
 			if (timeNow - drumTracker.setBeatToNowTime > 1000)
+			recentError = m.getArgAsFloat(0);
+			int size = drumTracker.beatDistribution.arraySize;
+			int newIndex = round((size/2)+(recentError*size));
+			double priorValue = drumTracker.beatDistribution.posterior[newIndex];
 			drumTracker.newKickError(m.getArgAsFloat(0), m.getArgAsFloat(2), m.getArgAsString(1));
+		
 			KLdiv = drumTracker.beatDistribution.getKLdivergence();
 			entropy = drumTracker.beatDistribution.getEntropyOfPosterior();
-			recentError = m.getArgAsFloat(0);
+			
 			drumType = m.getArgAsString(1);
+			beatPosition = m.getArgAsFloat(3);
+			printf("NEW BEAT: time %f error %f, position %f, drum %s,  ", m.getArgAsFloat(2), recentError, beatPosition,  drumType.c_str());
+			
+			printf("KL div %f, entropy %f prior value %f, ", KLdiv, entropy, priorValue);
+			
+			printf("tatum is %f\n", drumTracker.beatTimes.tatum );
 		}//end if new error
 		
 
@@ -1549,3 +1518,48 @@
 }
 */
 
+/*
+ beatDistribution.initialiseArray();
+ tempoDistribution.initialiseArray();
+ beatTimes.lastBeatTime = 0;
+ correctionFactor = 0.5;
+ 
+ 
+ 
+ tempoDistribution.likelihoodStdDev =  ARRAY_SIZE / 32;
+ //	tempoDistribution.likelihoodNoise = 0.96;
+ tempoDistribution.likelihoodNoise = 0.7;	
+ tempoDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);//wide 
+ 
+ beatDistribution.likelihoodStdDev = ARRAY_SIZE  / 32;
+ beatDistribution.likelihoodNoise = 0.56;	
+ beatDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/1);
+ 
+ 
+ tempoMinimum = 180;
+ tempoMaximum = 400;
+ posteriorMaximum = 0.1;
+ 
+ */
+
+/*
+ beatTimes.startIndex = 0;
+ beatTimes.lastBeatTime = 0;
+ maxPhase = 0;
+ posteriorMaximum = 0.1;
+ 
+ accompanimentStarted = false;
+ 
+ tempoDistribution.likelihoodNoise = 0.8;	
+ tempoDistribution.setGaussianPrior(ARRAY_SIZE/2, ARRAY_SIZE/2);//wide 
+ 
+ beatDistribution.initialiseArray();
+ tempoDistribution.initialiseArray();
+ 
+ tempoDistribution.calculateStandardDeviation();
+ beatDistribution.calculateStandardDeviation();
+ 
+ tempoStdDev = tempoDistribution.standardDeviation;
+ 
+ beatTimes.resetBeatTimeArray();
+ */
\ No newline at end of file
--- a/newOFsrc/testApp.h	Tue Mar 06 14:15:35 2012 +0000
+++ b/newOFsrc/testApp.h	Tue Mar 06 17:04:16 2012 +0000
@@ -124,6 +124,8 @@
 
 	double KLdiv, entropy, recentError;
 	string drumType;
+	double beatPosition;
+	
 	void drawKLdivAndEntropy();
 	private: