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egs/chime4/s5_1ch/local/CHiME3_simulate_data_patched_parallel.m
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function CHiME3_simulate_data_patched_parallel(official,nj,chime4_dir,chime3_dir)
% CHIME3_SIMULATE_DATA Creates simulated data for the 3rd CHiME Challenge
%
% CHiME3_simulate_data
% CHiME3_simulate_data(official)
%
% Input:
% official: boolean flag indicating whether to recreate the official
% Challenge data (default) or to create new (non-official) data
%
% If you use this software in a publication, please cite:
%
% Jon Barker, Ricard Marxer, Emmanuel Vincent, and Shinji Watanabe, The
% third 'CHiME' Speech Separation and Recognition Challenge: Dataset,
% task and baselines, submitted to IEEE 2015 Automatic Speech Recognition
% and Understanding Workshop (ASRU), 2015.
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Copyright 2015 University of Sheffield (Jon Barker, Ricard Marxer)
% Inria (Emmanuel Vincent)
% Mitsubishi Electric Research Labs (Shinji Watanabe)
% This software is distributed under the terms of the GNU Public License
% version 3 (http://www.gnu.org/licenses/gpl.txt)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
utils_folder = sprintf('%s/tools/utils', chime4_dir);
enhancement_folder = sprintf('%s/tools/enhancement/', chime3_dir);
addpath(utils_folder,'-end');
addpath(enhancement_folder);
sim_folder = sprintf('%s/tools/simulation', chime4_dir);
addpath(sim_folder);
upath = sprintf('%s/data/audio/16kHz/isolated/', chime4_dir);
cpath = sprintf('%s/data/audio/16kHz/embedded/', chime4_dir);
bpath = sprintf('%s/data/audio/16kHz/backgrounds/', chime4_dir);
apath = sprintf('%s/data/annotations/', chime4_dir);
upath_ext = 'local/nn-gev/data/audio/16kHz/isolated_ext/';
upath_simu = 'local/nn-gev/data/audio/16kHz/isolated/';
nchan=6;
% Define hyper-parameters
pow_thresh=-20; % threshold in dB below which a microphone is considered to fail
wlen_sub=256; % STFT window length in samples
blen_sub=4000; % average block length in samples for speech subtraction (250 ms)
ntap_sub=12; % filter length in frames for speech subtraction (88 ms)
wlen_add=1024; % STFT window length in samples for speaker localization
del=-3; % minimum delay (0 for a causal filter)
%% Create simulated training dataset from original WSJ0 data %%
if exist('equal_filter.mat','file'),
load('equal_filter.mat');
else
% Compute average power spectrum of booth data
nfram=0;
bth_spec=zeros(wlen_sub/2+1,1);
sets={'tr05' 'dt05'};
for set_ind=1:length(sets),
set=sets{set_ind};
mat=json2mat([apath set '_bth.json']);
for utt_ind=1:length(mat),
oname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_BTH'];
fprintf('%s
',[upath set '_bth/' oname '.CH0.wav']);
o=audioread([upath set '_bth/' oname '.CH0.wav']);
O=stft_multi(o.',wlen_sub);
nfram=nfram+size(O,2);
bth_spec=bth_spec+sum(abs(O).^2,2);
end
end
bth_spec=bth_spec/nfram;
% Compute average power spectrum of original WSJ0 data
nfram=0;
org_spec=zeros(wlen_sub/2+1,1);
olist=dir([upath 'tr05_org/*.wav']);
for f=1:length(olist),
oname=olist(f).name;
o=audioread([upath 'tr05_org/' oname]);
O=stft_multi(o.',wlen_sub);
nfram=nfram+size(O,2);
org_spec=org_spec+sum(abs(O).^2,2);
end
org_spec=org_spec/nfram;
% Derive equalization filter
equal_filter=sqrt(bth_spec./org_spec);
save('equal_filter.mat','equal_filter');
end
% Read official annotations
if official,
mat=json2mat([apath 'tr05_simu.json']);
% Create new (non-official) annotations
else
mat=json2mat([apath 'tr05_org.json']);
ir_mat=json2mat([apath 'tr05_real.json']);
for utt_ind=1:length(mat),
oname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_ORG'];
osize=audioread([upath 'tr05_org/' oname '.wav'],'size');
dur=osize(1)/16000;
envirs={'BUS' 'CAF' 'PED' 'STR'};
envir=envirs{randperm(4,1)}; % draw a random environment
mat{utt_ind}.environment=envir;
blist=dir([bpath '*' envir '.CH1.wav']);
dur_diff=inf(1,length(ir_mat));
for ir_ind=1:length(ir_mat),
if strcmp(ir_mat{ir_ind}.environment,envir),
ir_dur=ir_mat{ir_ind}.end-ir_mat{ir_ind}.start;
dur_diff(ir_ind)=abs(ir_dur-dur);
end
end
ir_ind=find(isinf(dur_diff));
ir_ind=ir_ind(1);
nfail=true;
while nfail,
bname=blist(randperm(length(blist),1)).name(1:end-8); % draw a random background recording
mat{utt_ind}.noise_wavfile=bname;
bsize=audioread([bpath bname '.CH1.wav'],'size');
bdur=bsize(1)/16000;
mat{utt_ind}.noise_start=ceil(rand(1)*(bdur-dur)*16000)/16000; % draw a random time
mat{utt_ind}.noise_end=mat{utt_ind}.noise_start+dur;
nname=mat{utt_ind}.noise_wavfile;
nbeg=round(mat{utt_ind}.noise_start*16000)+1;
nend=round(mat{utt_ind}.noise_end*16000);
n=zeros(nend-nbeg+1,nchan);
for c=1:nchan,
n(:,c)=audioread([bpath nname '.CH' int2str(c) '.wav'],[nbeg nend]);
end
npow=sum(n.^2,1);
npow=10*log10(npow/max(npow));
nfail=any(npow<=pow_thresh); % check for microphone failure
end
xfail=true;
while xfail,
dur_diff(ir_ind)=inf;
[~,ir_ind]=min(dur_diff); % pick impulse response from the same environment with the closest duration
if dur_diff(ir_ind)==inf,
keyboard;
end
mat{utt_ind}.ir_wavfile=ir_mat{ir_ind}.wavfile;
mat{utt_ind}.ir_start=ir_mat{ir_ind}.start;
mat{utt_ind}.ir_end=ir_mat{ir_ind}.end;
iname=mat{utt_ind}.ir_wavfile;
ibeg=round(mat{utt_ind}.ir_start*16000)+1;
iend=round(mat{utt_ind}.ir_end*16000);
x=zeros(iend-ibeg+1,nchan);
for c=1:nchan,
x(:,c)=audioread([cpath iname '.CH' int2str(c) '.wav'],[ibeg iend]);
end
xpow=sum(x.^2,1);
xpow=10*log10(xpow/max(xpow));
xfail=any(xpow<=pow_thresh); % check for microphone failure
end
mat{utt_ind}=orderfields(mat{utt_ind});
end
mat2json(mat,[apath 'tr05_simu_new.json']);
end
p = parpool('local', nj);
% Loop over utterances
parfor utt_ind=1:length(mat),
if official,
udir=[upath_simu 'tr05_' lower(mat{utt_ind}.environment) '_simu/'];
udir_ext=[upath_ext 'tr05_' lower(mat{utt_ind}.environment) '_simu/'];
else
udir=[upath 'tr05_' lower(mat{utt_ind}.environment) '_simu_new/'];
end
if ~exist(udir,'dir'),
system(['mkdir -p ' udir]);
end
if ~exist(udir_ext,'dir'),
system(['mkdir -p ' udir_ext]);
end
oname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_ORG'];
iname=mat{utt_ind}.ir_wavfile;
nname=mat{utt_ind}.noise_wavfile;
uname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_' mat{utt_ind}.environment];
ibeg=round(mat{utt_ind}.ir_start*16000)+1;
iend=round(mat{utt_ind}.ir_end*16000);
nbeg=round(mat{utt_ind}.noise_start*16000)+1;
nend=round(mat{utt_ind}.noise_end*16000);
% Load WAV files
fprintf('%s
',[upath 'tr05_org/' oname '.wav']);
o=audioread([upath 'tr05_org/' oname '.wav']);
[r,fs]=audioread([cpath iname '.CH0.wav'],[ibeg iend]);
fprintf('%s
',[cpath iname '.CH0.wav'],[ibeg iend]);
x=zeros(iend-ibeg+1,nchan);
n=zeros(nend-nbeg+1,nchan);
for c=1:nchan,
fprintf('%s Place1
',[cpath iname '.CH' int2str(c) '.wav']);
x(:,c)=audioread([cpath iname '.CH' int2str(c) '.wav'],[ibeg iend]);
n(:,c)=audioread([bpath nname '.CH' int2str(c) '.wav'],[nbeg nend]);
fprintf('%s Place2
',[bpath nname '.CH' int2str(c) '.wav']);
end
% Compute the STFT (short window)
O=stft_multi(o.',wlen_sub);
R=stft_multi(r.',wlen_sub);
X=stft_multi(x.',wlen_sub);
% Estimate 88 ms impulse responses on 250 ms time blocks
A=estimate_ir(R,X,blen_sub,ntap_sub,del);
% Derive SNR
Y=apply_ir(A,R,del);
y=istft_multi(Y,iend-ibeg+1).';
SNR=sum(sum(y.^2))/sum(sum((x-y).^2));
% Equalize microphone
[~,nfram]=size(O);
O=O.*repmat(equal_filter,[1 nfram]);
o=istft_multi(O,nend-nbeg+1).';
% Compute the STFT (long window)
O=stft_multi(o.',wlen_add);
X=stft_multi(x.',wlen_add);
[nbin,nfram] = size(O);
% Localize and track the speaker
[~,TDOAx]=localize(X);
% Interpolate the spatial position over the duration of clean speech
TDOA=zeros(nchan,nfram);
for c=1:nchan,
TDOA(c,:)=interp1(0:size(X,2)-1,TDOAx(c,:),(0:nfram-1)/(nfram-1)*(size(X,2)-1));
end
% Filter clean speech
Ysimu=zeros(nbin,nfram,nchan);
for f=1:nbin,
for t=1:nfram,
Df=sqrt(1/nchan)*exp(-2*1i*pi*(f-1)/wlen_add*fs*TDOA(:,t));
Ysimu(f,t,:)=permute(Df*O(f,t),[2 3 1]);
end
end
ysimu=istft_multi(Ysimu,nend-nbeg+1).';
% Normalize level and add
ysimu=sqrt(SNR/sum(sum(ysimu.^2))*sum(sum(n.^2)))*ysimu;
xsimu=ysimu+n;
% Write WAV file
for c=1:nchan,
audiowrite([udir uname '.CH' int2str(c) '.wav'],xsimu(:,c),fs);
audiowrite([udir_ext uname '.CH' int2str(c) '.Noise.wav'],n(:, c),fs);
audiowrite([udir_ext uname '.CH' int2str(c) '.Clean.wav'],ysimu(:, c), fs);
end
end
%% Create simulated development and test datasets from booth recordings %%
sets={'dt05' 'et05'};
for set_ind=1:length(sets),
set=sets{set_ind};
% Read official annotations
if official,
mat=json2mat([apath set '_simu.json']);
% Create new (non-official) annotations
else
mat=json2mat([apath set '_real.json']);
clean_mat=json2mat([apath set '_bth.json']);
for utt_ind=1:length(mat),
for clean_ind=1:length(clean_mat), % match noisy utterance with same clean utterance (may be from a different speaker)
if strcmp(clean_mat{clean_ind}.wsj_name,mat{utt_ind}.wsj_name),
break;
end
end
noise_mat=mat{utt_ind};
mat{utt_ind}=clean_mat{clean_ind};
mat{utt_ind}.environment=noise_mat.environment;
mat{utt_ind}.noise_wavfile=noise_mat.wavfile;
dur=mat{utt_ind}.end-mat{utt_ind}.start;
noise_dur=noise_mat.end-noise_mat.start;
pbeg=round((dur-noise_dur)/2*16000)/16000;
pend=round((dur-noise_dur)*16000)/16000-pbeg;
mat{utt_ind}.noise_start=noise_mat.start-pbeg;
mat{utt_ind}.noise_end=noise_mat.end+pend;
mat{utt_ind}=orderfields(mat{utt_ind});
end
mat2json(mat,[apath set '_simu_new.json']);
end
% Loop over utterances
parfor utt_ind=1:length(mat),
if official,
udir=[upath_simu set '_' lower(mat{utt_ind}.environment) '_simu/'];
udir_ext=[upath_ext set '_' lower(mat{utt_ind}.environment) '_simu/'];
else
udir=[upath set '_' lower(mat{utt_ind}.environment) '_simu_new/'];
end
if ~exist(udir,'dir'),
system(['mkdir -p ' udir]);
end
if ~exist(udir_ext,'dir'),
system(['mkdir -p ' udir_ext]);
end
oname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_BTH'];
nname=mat{utt_ind}.noise_wavfile;
uname=[mat{utt_ind}.speaker '_' mat{utt_ind}.wsj_name '_' mat{utt_ind}.environment];
tbeg=round(mat{utt_ind}.noise_start*16000)+1;
tend=round(mat{utt_ind}.noise_end*16000);
% Load WAV files
o=audioread([upath set '_bth/' oname '.CH0.wav']);
[r,fs]=audioread([cpath nname '.CH0.wav'],[tbeg tend]);
nsampl=length(r);
x=zeros(nsampl,nchan);
for c=1:nchan,
x(:,c)=audioread([cpath nname '.CH' int2str(c) '.wav'],[tbeg tend]);
end
% Compute the STFT (short window)
R=stft_multi(r.',wlen_sub);
X=stft_multi(x.',wlen_sub);
% Estimate 88 ms impulse responses on 250 ms time blocks
A=estimate_ir(R,X,blen_sub,ntap_sub,del);
% Filter and subtract close-mic speech
Y=apply_ir(A,R,del);
y=istft_multi(Y,nsampl).';
level=sum(sum(y.^2));
n=x-y;
% Compute the STFT (long window)
O=stft_multi(o.',wlen_add);
X=stft_multi(x.',wlen_add);
[nbin,nfram] = size(O);
% Localize and track the speaker
[~,TDOAx]=localize(X);
% Interpolate the spatial position over the duration of clean speech
TDOA=zeros(nchan,nfram);
for c=1:nchan,
TDOA(c,:)=interp1(0:size(X,2)-1,TDOAx(c,:),(0:nfram-1)/(nfram-1)*(size(X,2)-1));
end
% Filter clean speech
Ysimu=zeros(nbin,nfram,nchan);
for f=1:nbin,
for t=1:nfram,
Df=sqrt(1/nchan)*exp(-2*1i*pi*(f-1)/wlen_add*fs*TDOA(:,t));
Ysimu(f,t,:)=permute(Df*O(f,t),[2 3 1]);
end
end
ysimu=istft_multi(Ysimu,nsampl).';
% Normalize level and add
ysimu=sqrt(level/sum(sum(ysimu.^2)))*ysimu;
xsimu=ysimu+n;
% Write WAV file
for c=1:nchan,
audiowrite([udir uname '.CH' int2str(c) '.wav'],xsimu(:,c),fs);
audiowrite([udir_ext uname '.CH' int2str(c) '.Noise.wav'],n(:, c),fs);
audiowrite([udir_ext uname '.CH' int2str(c) '.Clean.wav'],ysimu(:, c), fs);
end
end
end
delete(p);
end
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