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learn_gmmtmap.m
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learn_gmmtmap.m
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clear all;
% Create the mapping table first by running
load FileWav16kHz
% see create_gmm for what to be loaded by FileWav16kHz
doNormalize = 1;
Nmix = 4;
SaveName = sprintf('Gmm%dClassTm', Nmix);
if doNormalize
SaveName = [SaveName, 'N'];
end
SampleOffset = 120; % Discard some samples (at the end, most samples are impulsive)
%% Class definition
% Gender
Class(1, 1).name = 'Male';
Class(1, 1).path = '.\MappingTable\GT100\N3W180R0S0EdN1\english]english239.male.N_english.R_usa.Y18.A19';
Class(1, 1).IsTrain = remove_samepath(IsTrain.IsMale, Class(1, 1).path, accnames);
Class(1, 1).IsTest = remove_samepath(IsTest.IsMale, Class(1, 1).path, accnames);
Class(1, 2).name = 'Female';
Class(1, 2).path = '.\MappingTable\GT100\N3W180R0S0EdN1\english]english10.female.N_english.R_usa.Y35.A35';
Class(1, 2).IsTrain = remove_samepath(IsTrain.IsFemale, Class(1, 2).path, accnames);
Class(1, 2).IsTest = remove_samepath(IsTest.IsFemale, Class(1, 2).path, accnames);
% Native Male
Class(2, 1).name = 'NativeMaleRef';
Class(2, 1).path = '.\MappingTable\GT100\N3W180R0S0EdN1\english]english451.male.N_english.R_usa.Y44.A44';
Class(2, 1).IsTrain = remove_samepath(IsTrain.IsNative, Class(2, 1).path, accnames);
Class(2, 1).IsTest = remove_samepath(IsTest.IsNative, Class(2, 1).path, accnames);
Class(2, 2).name = 'NonNativeMaleRef';
Class(2, 2).path = '.\MappingTable\GT100\N3W180R0S0EdN1\japanese]japanese4.male.N_japanese.R_usa.Y1.A20';
Class(2, 2).IsTrain = remove_samepath(IsTrain.IsNonNative, Class(2, 2).path, accnames);
Class(2, 2).IsTest = remove_samepath(IsTest.IsNonNative, Class(2, 2).path, accnames);
% Native Female
Class(3, 1).name = 'NativeFemaleRef';
Class(3, 1).path = '.\MappingTable\GT100\N3W180R0S0EdN1\english]english165.female.N_english.R_usa.Y43.A43';
Class(3, 1).IsTrain = remove_samepath(IsTrain.IsNative, Class(3, 1).path, accnames);
Class(3, 1).IsTest = remove_samepath(IsTest.IsNative, Class(3, 1).path, accnames);
Class(3, 2).name = 'NonNativeFemaleRef';
Class(3, 2).path = '.\MappingTable\GT100\N3W180R0S0EdN1\japanese]japanese26.female.N_japanese.R_usa.Y6.A44';
Class(3, 2).IsTrain = remove_samepath(IsTrain.IsNonNative, Class(3, 2).path, accnames);
Class(3, 2).IsTest = remove_samepath(IsTest.IsNonNative, Class(3, 2).path, accnames);
%% Create Concatenated vector
AV = accdata_read(Class, accnames, 'Train', 100);
AVTest = accdata_read(Class, accnames, 'Test', 100);
%% Training GMM
D = 100;
[NClassDef, NGroup] = size(Class);
for iClassDef = 1:NClassDef
for FromGroup = 1:NGroup
for RefGroup = 1 : NGroup
x = AV(iClassDef, FromGroup, RefGroup).Vecs;
% plot(X);
% pause;
% X = x;
X = resample(x, D, length(x));
[Label, Gmm, llh] = emgm(X, Nmix);
GmmClass(iClassDef, FromGroup, RefGroup).Gmm = Gmm;
GmmClass(iClassDef, FromGroup, RefGroup).Label = Label;
GmmClass(iClassDef, FromGroup, RefGroup).llh = llh;
end
end
end
% norm(SvdClass(1, 1, 1).U - SvdClass(1, 2, 1).U)
% norm(SvdClass(1, 1, 2).U - SvdClass(1, 2, 2).U)
% norm(SvdClass(1, 1, 1).U - SvdClass(1, 2, 1).U)
% fprintf('SVD Computation completed and Saving the data\n');
% save(SaveName, 'SvdClass', 'Class', 'SampleOffset');
%% Classify GMM
[NClassDef, NGroup] = size(Class);
PC = 0;
for iClassDef = 1:NClassDef
for FromGroup = 1:NGroup
for RefGroup = 1 : NGroup
x = AVTest(iClassDef, FromGroup, RefGroup).Vecs;
X = resample(x, D, length(x));
Nsamples = size(x, 2);
P1 = zeros(1, Nsamples);
P2 = zeros(1, Nsamples);
for k = 1:Nsamples
P1(k) = gmmpdf(X(:, k), GmmClass(iClassDef, 1, RefGroup).Gmm);
P2(k) = gmmpdf(X(:, k), GmmClass(iClassDef, 2, RefGroup).Gmm);
end
if FromGroup==1
pc = sum((P1 > P2))/Nsamples;
else
pc = sum((P2 > P1))/Nsamples;
end
% PC(iClassDef, FromGroup, RefGroup) = pc;
PC(FromGroup, RefGroup, iClassDef) = pc;
% Gr(iClassDef).Cat(FromGroup).Pr(RefGroup, :) = Pr;
end
end
% max(Gr(iClassDef).Cat(RefGroup).P(:, 1) > max(Gr(iClassDef).Cat(RefGroup).P(:, 2)
end
mean(PC(:, :, 1))
mean([PC(:, :, 2); PC(:, :, 3 )])
%% SVD Classification Rule
% For Gender, There are 4 different groups of features,
% A1) M -> M; 1 -> 1
% A2) F -> M; 2 -> 1
% B1) M -> F; 1 -> 2
% B2) F -> F; 2 -> 1
% For Male Speaker,
% test Err(X | M -> M) < Err(X | F -> M)
% +) test Err(X | M -> F) < Err(X | F -> F)
% For Female Speaker
% test Err(X | M -> M) > Err(X | F -> M)
% +) test Err(X | M -> F) > Err(X | F -> F)
% Generalize
% test Err(X1 | A1 ) < Err(X1 | A2 )
% +) test Err(X1 | B1 ) < Err(X1 | B2 )
%
% test Err(X2 | A1 ) > Err(X2 | A2 )
% +) test Err(X2 | B1 ) > Err(X2 | B2 )
%
% load SvdClassTmN
[NClassDef, NGroup] = size(Class);
clear Err;
for iClassDef = 1 : NClassDef
for TestGroup = 1 : NGroup
Fidx = find(Class(iClassDef, TestGroup).IsTest);
for RefGroup = 1 : NGroup
for i = 1:length(Fidx)
fname = fullfile(Class(iClassDef, RefGroup).path, [accnames{Fidx(i)}, '.mat']);
load(fname); % load 'GTpoly1'
% Y = resample(GTpoly1, D, length(GTpoly1));
Y = GTpoly1;
pp1 = gmmpdf(Y, GmmClass(iClassDef, 1, RefGroup).Gmm);
pp2 = gmmpdf(Y, GmmClass(iClassDef, 2, RefGroup).Gmm);
PrXIC(i, 1) = pp1;
PrXIC(i, 2) = pp2;
end
disp('dfd');
pause;
end
end
end
for iClassDef = 1 : NClassDef
for TestGroup = 1 : NGroup
A1 = [Err(iClassDef, TestGroup, 1, 1, :).n2];
A2 = [Err(iClassDef, TestGroup, 2, 1, :).n2];
B1 = [Err(iClassDef, TestGroup, 1, 2, :).n2];
B2 = [Err(iClassDef, TestGroup, 2, 2, :).n2];
[MinVal, MinIdx] = min([A1; A2; B1; B2]);
% Report the percent correct if MinIdx is either A1 or A2, i.e., 1 or 3
if TestGroup == 1
PC(iClassDef, TestGroup) = (sum(MinIdx==1) + sum(MinIdx==3))/length(MinIdx);
elseif TestGroup == 2
PC(iClassDef, TestGroup) = (sum(MinIdx==2) + sum(MinIdx==4))/length(MinIdx);
end
end
end
PC