EE 443 Lab 3

GMM Library: https://github.com/snitish/libgmm SVM Library: https://www.csie.ntu.edu.tw/~cjlin/libsvm/

Problem 1: (Matlab) MFCC

Status: Complete

• getMFCC.m
• Frame sounds using A=21ms windows, B=10ms shift, and A-B=11ms of overlapping. Generate 13 MFCC coefficients for every frame. (4 seconds of sounds will generate 380 frames, that is the sample will have 380x13 MFCC coefficients matrix). Plot the 380x13 MFCC coefficients for all three Bird sounds in Matlab.

Problem 2: (Matlab) Training GMM using MFCC features

Status: Complete

• gmm_traning.m
• prints to txt means, variances, weights
• Find the GMM parameters of the features found in Problem 1 to cluster the features.

GMM parameters: In Matlab, 'CovarianceType', 'diagonal' to generate k times D covariances, where k is the number of classes, and D is the number of features.

GMModel = fitgmdist(X,k,'CovarianceType','diagonal');

	Matlab	gmm.c
Means	GMModel.mu	GMM.means
Variances	GMModel.Sigma	GMM.covars
Weights	GMModel.PComponents	GMM.weights

Problem 3: (LCDK) Classification of Bird sound using GMM

Status: Complete

• correctly (most of the time) IDs bluejay and dove; duck correctly IDed
• gmm_write.m
• lab3_problem3 directory (main.c & ISRs.c)--gmm.h array lengths for means, covars, P(k|x) changed to 39; not sure why JP hard coded it and changed it from the memory allocation performed by the original.
• Use GMM model trained in Problem 2 to classify the Bird sound received through Line input of LCDK.

Problem 4: (Matlab) Training SVM for Bird sound classification

Status: Complete

• svm_training.m
• Train the SVM model using the features found in Problem 1.

SVM parameters: In Matlab, 'SaveSupportVectors', 'on' helps to save the support vectors in a SVM model.

t = templateSVM('SaveSupportVectors','on')
Mdl = fitcecoc(X,Y,'Learners',t);

	Matlab	svm.cpp
Number of Classes	3	model->nr_class
Support Vectors	Md1.BinaryLearners{1}.SupportVectors	model->SV
	Md1.BinaryLearners{2}.SupportVectors
	Md1.BinaryLearners{3}.SupportVectors
Number of Support Vectors	length(SVMmodel.BinaryLearners{1}.SupportVectors)	model->nSV
	length(SVMmodel.BinaryLearners{2}.SupportVectors)
	length(SVMmodel.BinaryLearners{3}.SupportVectors)
Alpha	Md1.BinaryLearners{1}.Alpha	model->sv_coef
	Md1.BinaryLearners{2}.Alpha
	Md1.BinaryLearners{3}.Alpha
Bias	Md1.BinaryLearners{1}.Bias	model->rho
	Md1.BinaryLearners{2}.Bias
	Md1.BinaryLearners{3}.Bias

Problem 5: (LCDK) Classification of Bird sound using SVM

Status: Complete

• svm_write.m
• svm_training.m
• lab3_problem5 directory (main.c & ISRs.c)--debugging!
• SVM to classify the features. Use MFCC features to classify Bird sounds.