Commit eb9b0a75 authored by Joanna Luberadzka's avatar Joanna Luberadzka

blabla

parent a5937feb
......@@ -47,7 +47,7 @@ SNR=[-10 -5 0 5 Inf];
end
end
sTrainingParam.N=size(m_YOut_train,1);
sTrainingParam.N=size(m_YOut_train,1)
% Initialize weights
v_m=sum(m_YOut_train)/sTrainingParam.N;
v_v=sum(bsxfun(@minus,m_YOut_train,v_m).^2)/sTrainingParam.N;
......@@ -68,11 +68,11 @@ end
%Compute weights
% [m_W, ~, ~]=trainNeuralNetwork(m_W,m_YOut_train,sTrainingParam.epsilon,sTrainingParam.display);
[m_W, ~, ~]=trainNeuralNetwork(m_W,m_YOut_train,sTrainingParam.epsilon,sTrainingParam.display);
% [m_W, m_DeltaW_av, m_W_av, v_LogLikelihood]=trainNeuralNetwork(m_W,m_YOut_train,epsilon);
% save('m_W.mat','m_W');
load('m_W.mat');
% load('m_W.mat');
%Compute B_matrix
m_B = B_matrix(m_labels_train', m_YOut_train, m_W,sTrainingParam.M);
......
......@@ -18,7 +18,7 @@ addpath(genpath('/media/joanna/daten/user/joanna/Data/TIMIT/Uncertainty_Data/'))
% of concatenated MEL spectrum. The signals used to obtain TRAINING data
% are anechoic and do not contain HRIR information at all.
ID=['TIMIT_conv_manySNRs',num2str(datestr(now,'-dd-mmm-yyyy'))];
ID=['TIMIT_conv_manySNRs2',num2str(datestr(now,'-dd-mmm-yyyy'))];
% training parameters
sTrainingParam=struct('datatype','spectral_slices', 'NperNoise',150000,'A',1000,'epsilon',0.1,...
......
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