P. Loizou, Speech Enhancement: Theory and Practice, 2nd Ed. (Taylor & Francis, Boca Raton, 2013).
Google Scholar
R. McAulay and M. Malpass, “Speech enhancement using a softdecision noise suppression filter,” IEEE Trans. Acoust., Speech, Signal Process. 28, 137–145 (1980).
Article
Google Scholar
N. S. Kim and J.-H. Chang, “Spectral enhancement based on global soft decision,” IEEE Signal Process. Lett. 7 (5), 108–110 (2000).
Article
Google Scholar
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean square error short-time spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal Process. 32, 1109–1121 (1984).
Article
Google Scholar
G.-H. Ding, T. Huang, and B. Xu, “Suppression of additive noise using a power spectral density MMSE estimator,” IEEE Signal Process. Lett. 11, 585–588 (2005).
Article
Google Scholar
R. Martin, “Speech enhancement based on minimum mean-square error estimation and supergaussian priors,” IEEE Trans. Speech Audio Process. 13, 845–856 (2005).
Article
Google Scholar
Y. Ephraim and D. Malah, “Speech enhancement using a minimum mean-square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Signal Process. 33, 443–445 (1985).
Article
Google Scholar
T. Lotter and P. Vary, “Speech enhancement by map spectral amplitude estimation using a super-Gaussian speech model,” EURASIP J. Appl. Signal Process., No. 7, 1110–1126 (2005).
Article
MATH
Google Scholar
L. Yaroslavsky and M. Eden, Fundamentals of Digital Optics (Birkhaeuser, Boston, 1996).
Book
MATH
Google Scholar
V. Kober, M. Mozerov, and J. Alvarez-Borrego, “Nonlinear filters with spatially connected neighborhoods,” Opt. Eng. 40, 971–983 (2001).
Article
Google Scholar
P. J. Huber, P. C. Pop, and E. M. Ronchetti, Robust Statistics, 2nd Ed., (Wiley, New York, 2009).
Book
MATH
Google Scholar
V. M. Diaz-Ramirez and V. Kober, “Robust speech processing using local adaptive nonlinear filtering,” IET Signal Process. 7, 345–359 (2013).
MathSciNet
Article
Google Scholar
“IEEE Subcommittee (1969), IEEE recommended practice for speech quality measurements,” IEEE Trans. Audio Electroacoust. 17, 225–246 (1969).
“ITU, Perceptual evaluation of speech quality (PESQ). An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs,” ITU-T Recommendation, 862 (2001).
C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “An algorithm for intelligibility prediction of time-frequency weighted noisy speech,” IEEE Trans. Audio, Speech, Language Process. 19, 2125–2136 (2011).
Article
Google Scholar
E. Vincent, R. Gribonval, and C. F‘evotte, “Performance measurement in blind audio source separation,” IEEE Trans. Audio, Speech, Language Process. 14, 1462–1469 (2006).
Article
Google Scholar