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stubI_MesslKeras.m
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stubI_MesslKeras.m
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function [Y data] = stubI_MesslKeras(X, fail, fs, inFile, loadDataDir, statsDir, beamformer, tdoaSrc, ncovSrc, I, maxSup_db, d_m,lstm_dir, maskmode)
% Load 6-channel LSTM masks and 1-channel MESSL mask.
% Generate speech mask, noise mask for beamforming and post-filter for post-processing.
% Apply beam-forming by using the speech mask and the noise mask.
% Apply the post-filter onto the enhanced single-channel spectrogram.
if ~exist('statsDir', 'var'), statsDir = ''; end
if ~exist('beamformer', 'var') || isempty(beamformer), beamformer = 'bestMic'; end
if ~exist('tdoaSrc', 'var') || isempty(tdoaSrc), tdoaSrc = 'dataItd'; end
if ~exist('ncovSrc', 'var') || isempty(ncovSrc), ncovSrc = 'mask'; end
if ~exist('I', 'var') || isempty(I), I = inf; end
if ~exist('maxSup_db', 'var') || isempty(maxSup_db), maxSup_db = 40; end
if ~exist('d_m', 'var') || isempty(d_m), d_m = 0.12; end
if ~exist('combineOpt') || isempty(combineOpt), combineOpt = 'average'; end
%inFile is the filename for the auido file, so make use of it to find LSTM mask file.
lstm_file = fullfile(lstm_dir, regexprep(inFile, '(\.CH1)?\.wav$', '.mat'));
% six masks need to combine to one mask
LSTM_Mask = load(lstm_file); %something like this
LSTM_Mask = LSTM_Mask.mask.';
%combine the LSTM mask
wlen = 2*(size(X,1)-1);
M = sum(~fail);
% Load MESSL data structure
refFile = fullfile(loadDataDir, regexprep(inFile, '(\.CH1)?\.wav$', '.mat'));
d = load(refFile);
if strcmp(tdoaSrc, 'ipd') || strcmp(ncovSrc, 'ipd')
tau = tauGrid(d_m, fs, 31); % Brittle
covs = covsFromIpdParams(X, d.data.params.ipdParams, M, tau, fs);
assert(~any(isnan(covs(:))))
end
Scov = [];
switch tdoaSrc
case 'recomputeItd'
channelPairs = nchoosek(1:sum(~fail), 2); % Brittle
tau = tauGrid(d_m, fs, 31); % Brittle
pTauI = cat(3, d.data.params.ipdParams.p_tauI);
perPairItd = squeeze(sum(bsxfun(@times, tau, pTauI), 2) ./ sum(pTauI,2))'; % posterior mean
perMicTdoa = perMicTdoaLs(perPairItd(:,size(d.data.params.perMicTdoa,2)), channelPairs);
case 'dataItd'
perMicTdoa = d.data.params.perMicTdoa;
case 'ipd'
Scov = covs(:,:,:,1);
otherwise
error('Unknown tdoa method: %s', tdoaSrc)
end
Ncov = [];
switch ncovSrc
case 'file'
% Load precomputed stats for noise covariance.
% Forces ignoring mask for noise covariance estimation
statsFile = fullfile(statsDir, regexprep(inFile, '(\.CH1)?\.wav', '.mat'));
if exist(statsFile, 'file')
stats = load(statsFile);
Ncov = stats.Ncov;
else
fprintf('No stats found for: "%s"\n', statsFile);
end
case 'mask'
% Ncov = [];
case 'ipd'
Ncov = covs(:,:,:,2);
otherwise
error('Unknown ncovSrc: %s', ncovSrc)
end
%Mix the MESSL mask with LSTM mask
%Or there could be multi options for the mixture, such as average, max, min
mask = d.data.mask(:,:,1:min(I,end)); %mask is initialized as messl mask
%only use the channel which doesn't fail
switch maskmode
case 'messl'
speech_mask = mask;
noise_mask = mask;
post_filter = mask;
case 'LSTM'
speech_mask = min(LSTM_Mask(:,:,~fail),[],3);
noise_mask = max(LSTM_Mask(:,:,~fail),[],3);
post_filter = mean(LSTM_Mask(:,:,~fail),3);
case 'average':
speech_mask = min(LSTM_Mask(:,:,~fail),mask,3);
noise_mask = max(LSTM_Mask(:,:,~fail),mask,3);
post_filter = mean(LSTM_Mask(:,:,~fail),mask,3);
% switch maskforSpeechOpt
% case 'average'
% LSTM_Mask = mean(LSTM_Mask,3); %make sure the axis is channel
% case 'max'
% LSTM_Mask = max(LSTM_Mask,[],3);
% case 'min'
% LSTM_Mask = min(LSTM_Mask,[],3);
% end;
% switch maskForNoiseOpt
% %need speech mask to be the min, the noise mask to be the max.
% %compare 7 masks in on opt
% case 'average'
% beamformingMask = (LSTM_Mask+mask)/2;
% case 'max'
% beamformingMask = max(LSTM_Mask,mask);
% case 'min'
% beamformingMask = min(LSTM_Mask,mask);
% case 'messl'
% beamformingMask = mask;
% case 'lstm'
% beamformingMask = LSTM_Mask;
% end;
% switch maskForPostFilterOpt
% %average of 6 LSTM, then average of LSTM + MESSL
% case 'average'
% postfilterMask = (LSTM_Mask+mask)/2;
% % case 'max'
% % postfilterMask = max(LSTM_Mask,mask);
% % case 'min'
% % postfilterMask = min(LSTM_Mask,mask);
% case 'messl'
% postfilterMask = mask;
% case 'lstm'
% postfilterMask = LSTM_Mask;
% end;
data.origDataFile = refFile;
switch beamformer
case 'lstm'
Xp = mvdrLSTMMulti(X, fail, speech_mask, noise_mask);
mask = post_filter;
case 'mic1'
Xp = repmat(X(:,:,1), [1 1 size(mask,3)]);
case 'mic2'
Xp = repmat(X(:,:,2), [1 1 size(mask,3)]);
case 'bestMic'
Xp = pickChanWithBestSnr(X, mask, fail);
case 'mvdr'
[Xp mvdrMask mask] = maskDrivenMvdrMulti(X, mask, fail, perMicTdoa, Ncov);
data.mvdrMask2 = mvdrMask;
case 'souden'
[Xp mvdrMask mask] = mvdrSoudenMulti(X, noise_mask, fail, Ncov, Scov);
mask = postfilterMask;
%[Xp2 mvdrMask mask] = mvdrSoudenMulti(X, postfilterMask, fail, Ncov, Scov);
data.mvdrMask2 = single(mvdrMask);
case 'souden0'
[Xp mvdrMask mask] = mvdrSoudenMulti(X, noise_mask, fail, Ncov, Scov, 0);
data.mvdrMask2 = single(mvdrMask);
otherwise
error('Unknown beamformer: %s', beamformer)
end
maxSup = 10^(-maxSup_db / 20);
postfilterMask = max(mask, maxSup);
data.beamformingMask = beamformingMask;
data.postfilterMask = postfilterMask;
data.params = d.data.params;
% Output spectrogram(s)
Y = Xp .* postfilterMask;
%subplots({db(Xp(:,:,1)), mask(:,:,1)})
1+1;