% This is a simple demo load('test_data'); % The speech data used in this demo is available: http://www.emime.org/participate/emime-bilingual-database % 1-3: Finnish speaker FM3 % 4-6: Finnish speaker FF3 file_num = 5; % 1-6 audio_wav = test_data.pink_wav{file_num}; % Load precomputed log-compressed mel-spectral features: feas1 = test_data.pink_pre{file_num}; % Mask estimation operates in the mel-spectral domain: mask1 = estimate_noise_mask(exp(feas1), audio_wav, test_data.fs, test_data.frame_ms, test_data.step_ms); % Reconstruction load('gmm_demo_emime'); % load pre-trained clean speech model % trained with the GMMBAYES toolbox: http://www.it.lut.fi/project/gmmbayes/ % GMM-MMSE % Bounded conditional mean imputation with TPMI reconstructed_feas1{1}=tpmi(feas1, mask1, gmm_model1, 0); % Bounded conditation mean imputation with TCMI reconstructed_feas1{2}=tcmi(feas1, mask1, gmm_model1, 0); % GMM-MAP if exist('qpc','dir') % Cluster-based imputation (this is not bounded conditional mean imputation, but a related method) reconstructed_feas1{3}=mapi(feas1, mask1, gmm_model1, 0); end clean_feas1 = test_data.clean_pre{file_num}; % load pre-computed clean speech features for reference show_reconstruction_results(clean_feas1, feas1, mask1, reconstructed_feas1, {'TPMI','TCMI','Cluster-based imputation'});
