Contents

% Present folder contains implementations of binary mask algorirthms or ideal channel selection (ICS)
% algorithms based on different selection criteria (as presented at CIAP'11
% conference in "Channel selection: A Panacea for the Interference Problem in Cochlear Implants").
% For the SNR selection criterion, ICS is also known in the
% literature as ideal binary mask (see review in book by Wang and Brown,
% 2006). References for the other selection criteria are provided at the
% end of this file, and are available from our website.
%
% Direct any inquiries about the code to: Philip Loizou ([email protected])
% =============================================================================

ICS DEMO

% Usage: ics(noisefile, clfile, outfile, nsnr, thrd)
%
% noisefile - name of masker file
% clfile - name of clean stimulus file
% outfile - name of output file
% nsnr is the overall input SNR (in dB) for noisy file
% thrd is the SNR threshold  (in dB)


% Example:
% In MATLAB, type:
%   >> ics('babble1.wav','S_01_01.wav','out.wav',-10,-5)

ics('babble1.wav','S_01_01.wav','out.wav',-10,-5)

% For the above example, input SNR=-10 dB and SNR threshold=-5 dB.
% The noisy (mixture) file is contained in 'S_01_01-noisy.wav' file.
% Segregated file is in 'out.wav'

% The wav files can be played via a Media Player, Cool Edit, Audition, etc.
% It can also be viewed and played through our toolbox 'Colea':
% http://www.utdallas.edu/~loizou/speech/software.htm

% Reference:
% Li, N. and Loizou, P. (2008). "Factors influencing intelligibility of ideal binary-masked speech: Implications
% for noise reduction," Journal of Acoustical Society of America, 123(3), 1673-1682


COMPETING-TALKER DEMO

% Usage: ics_competing_talker(filename, clfile, t_outfile, m_outfile,thrd)
%
% filename - mixture filename
% clfile - clean target filename
% t_outfile - output file: Target talker
% m_outfile - output file: Competing talker
% thrd - SNR threshold in dB

% Example:
%   >> ics_competing_talker('talker_mixture.wav', 'S_01_10.wav','target.wav','masker.wav',-5)

ics_competing_talker('talker_mixture.wav','S_01_10.wav','target.wav','masker.wav',-5)

% In 'talker_mixture.wav' mixture file, the competing talker was added
% at SNR=-5 dB (target and competing talkers were same for this example).
% Files 'target.wav' and 'masker.wav' contain processed sentences
% of the segregated target and competing-talker talkers respectively.
% SNR threshold=-5 dB.

CONSTRAINT RULE

% Usage: ics_constr_rule(filename, clfile, outfile, GAIN)
%
% filename - noisy speech filename (mixture)
% clfile - clean speech filename
% outilfe - name of output file
% GAIN='Wiener'; 'MMSE', 'logMMSE', 'MMSE-SPU'; 'pMMSE'; 'SpecSub'
%
% Example:
%   >> ics_constr_rule('S_01_02-babble_m10dB.wav', 'S_01_02.wav','out_constr.wav','Wiener')

ics_constr_rule('S_01_02-babble_m10dB.wav', 'S_01_02.wav','out_constr.wav','Wiener')

% Target was corrupted with babble at -10 dB SNR.
% The Wiener gain function was used.
% Other possible gain functions: 'MMSE', 'logMMSE', 'MMSE-SPU', 'pMMSE', 'SpecSub'

% Example with competing-talker:
%   >> ics_constr_rule('talker_mixture.wav','S_01_10.wav','out.wav','Wiener')

ics_constr_rule('talker_mixture.wav','S_01_10.wav','out.wav','Wiener')

% Another example in babble at input SNR=-5 dB
%   >> ics_constr_rule('S_02_02-babble_m5dB.wav', 'S_02_02.wav','out_constr.wav','Wiener')

ics_constr_rule('S_02_02-babble_m5dB.wav', 'S_02_02.wav','out_constr.wav','Wiener')

%References
%Kim, G. and Loizou, P. (2011). “Gain-induced speech distortions and the absence of intelligibility
%  benefit with existing noise-reduction algorithms,” J. Acoust. Soc. Am. 130(3), (in press)
%Loizou, P.  and Kim, G. (2011). "Reasons why Current Speech-Enhancement Algorithms do not Improve Speech
% Intelligibility and Suggested Solutions," IEEE Trans. Audio, Speech, Language Processing, 19(1), 47-56.

REVERBERATION RULE

% Usage:  ics_reverb(reverbfile, clfile, outfile, thrd)
%
% reverbfile - name of  file containing reverberated stimulus
% clfile - name of clean sentence file
% outfile - name of output (processed) file
% thrd is the threshold (in dB) for signal-to-reverberant ratio criterion

% Example:
%   >> ics_reverb('rev800_2.wav','clean_2.wav','outrev.wav',-8)

ics_reverb('rev800_2.wav','clean_2.wav','outrev.wav',-8)

% File was corrupted with RT60=0.8 sec reverberation.
% The signal-to-reverberant ratio (SRR) threshold was set to -8 dB.

% Reference
% Kokkinakis, K., Hazrati, O. and Loizou, P. (2011). "A channel-selection criterion for suppressing reverberation
% in cochlear implants," Journal of the Acoustical Society of America, 129(5), 3221-3232.

MASKER BASED RULE

 % Usage: ics_masker_rule(filename, clfile, outfile)

 

% filename - noisy speech filename (mixture)

% clfile - clean speech filename

% outilfe - name of output file

Example:

ics_masker_rule('S_01_01-noisy.wav','S_01_01.wav','out_masker_rule.wav')

%Reference:
%Kim, G. and Loizou, P. (2010). "A new binary mask based on noise constraints for improved speech intelligibility,"
% Proc. INTERSPEECH, Makuhari, Japan, pp. 1632-1635.

REFERENCES

% Publications (from our lab) assuming ideal conditions:
% =============================================================================
%
% Hu, Y. and Loizou, P. (2008).
% "A new sound coding strategy for suppressing noise in cochlear implants,"
% Journal of Acoustical Society of America, 124(1), 498-509.
%
% Kim, G. and Loizou, P. (2010).
% "A new binary mask based on noise constraints
% for improved speech intelligibility,"
% Proc. INTERSPEECH, Makuhari, Japan, pp. 1632-1635.
%
%Kim, G. and Loizou, P. (2011). “Gain-induced speech distortions and the absence of intelligibility
%  benefit with existing noise-reduction algorithms,” J. Acoust. Soc. Am. 130(3), (in press)
%
% Kokkinakis, K., Hazrati, O. and Loizou, P. (2011).
% "A channel-selection criterion for suppressing reverberation
% in cochlear implants,"
% Journal of the Acoustical Society of America, 129(5), 3221-3232.
%
% Li, N. and Loizou, P. (2008).
% "Factors influencing intelligibility of ideal binary-masked speech:
%  Implications for noise reduction,"
% Journal of Acoustical Society of America, 123(3), 1673:1682
%
% Li, N. and Loizou, P. (2008).
% "Effect of spectral resolution on the intelligibility
% of ideal binary masked speech,"
% Journal of Acoustical Society of America, 123(4), EL59- EL64
%
% Loizou, P.  and Kim, G. (2011).
% "Reasons why Current Speech-Enhancement Algorithms
% do not Improve Speech Intelligibility and Suggested Solutions,"
% IEEE Trans. Audio, Speech, Language Processing, 19(1), 47-56.


% Publications (from our lab) assuming realistic conditions:
% =============================================================================
%
% Hu, Y. and Loizou, P. (2008).
% "Techniques for estimating the ideal binary mask,"
% Proc. of 11th International Workshop on Acoustic Echo and Noise Control,
% September 14th-17th, Seattle, Washington.
%
% Hu, Y. and Loizou, P. (2010).
% "Environment-specific noise suppression for improved speech
% intelligibility by cochlear implant users,"
% Journal of the Acoustical Society of America, 127(6), 3689-3695.
%
% Kim, G., Lu, Y., Hu, Y. and Loizou, P. (2009).
% "An algorithm that improves speech intelligibility in noise for
% normal-hearing listeners,"
% Journal of the Acoustical Society of America, 126(3), 1486-1494
%
% Kim, G. and Loizou, P. (2010).
% "Improving Speech Intelligibility in Noise
% Using Environment-Optimized Algorithms,"
% IEEE Trans. Audio, Speech, Language Processing, 18(8), 2080-2090.
%
% Kim, G. and Loizou, P. (2010).
% "Improving Speech Intelligibility in Noise Using a Binary Mask
% that is Based on Magnitude Spectrum Constraints,"
% IEEE Signal Processing Letters, 17(2), 1010-1013
%
% Kim, G. and Loizou, P. (2009).
% "A data-driven approach for estimating the time-frequency binary mask,"
% Proc. Interspeech, Brighton, UK, Sept 6-9, 2009