1. Introduction 2. Extraction des descripteurs

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TP!Apprentissage!Automatique!
Date!:!!-9!:(35!-./-!
Auteur!:!:(4;%,6!<(&3('&,!
1.#Introduction#
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>> addpath(‘netlab’)
2.#Extraction#des#descripteurs#
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[d,sr] = wavread(fullfile('music','3.wav'));
soundsc(d,sr);
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[stt,dur,lab] = textread(fullfile('labels','3.lab'), '%f %f
%s','commentstyle','shell');
[stt(1:4),dur(1:4)] % regardons des données un peu
ans = 0 3.3470
3.3470 1.0540
4.4010 2.6190
7.0200 1.2860
lab(1:4)
ans =
'vox'
'mus'
'vox'
'mus'
% donc 3.wav commence par 3.35 de voix, ensuite la musique, etc.
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% MFCCs on a 20ms timebase:
cc = mfcc(d,sr,1/0.020);
% How big is it?
size(cc)
ans = 13 749
% First 13 cepstra (0..12) is standard;
% Take a look at the cepstra:
figure,imagesc(cc)
axis xy
% It's hard to see much in the cepstra.
% C0 is scaled differently because it's the average.
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>> cc = [cc; deltas(cc); deltas(deltas(cc,5),5)];
>> size(cc)
ans = 39 11987
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load ftrslabs.mat
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3.#Modèle#de#mélange#Gaussien#
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% Sort training data into sung and unsung frames
ddS = ftrs(labs==1,:);
ddM = ftrs(labs==0,:);
% Compare scatter plots of 1st 2 dimensions
% First with sung frames (red dots) in front:
subplot(221)
plot(ddM(:,1),ddM(:,2),'.b',ddS(:,1),ddS(:,2),'.r')
% then with unsung frames (blue dots) in front:
subplot(222)
plot(ddS(:,1),ddS(:,2),'.r',ddM(:,1),ddM(:,2),'.b')
% Heavily overlapped, but some difference...
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[M0,M1] = traingmms(ftrs(:,[1 2]), labs, 5);
Warning: Maximum number of iterations has been exceeded
Warning: Maximum number of iterations has been exceeded
Warning: Maximum number of iterations has been exceeded
Warning: Maximum number of iterations has been exceeded
Accuracy on training data = 66.1%
Elapsed time = 36.7439 secs
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4.#Évaluation#
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>> load evaldata.mat % va charger: cc lsamp tt
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5.#Malédiction#de#la#dimensionalité#
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6.#Classification#non#paramétrique#
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