Selective-Tap Blind Dereverberation for Two-Microphone Enhancement of Reverberant Speech

IEEE Signal Processing Letters

Volumn 16, Issue 11, 2009, Pages 961-964

  Kokkinakis, Kostas ^a   Loizou, Philipos ^a  

^a The University of Texas at Dallas   (United States)

Author keywords

Blind dereverberation; least mean square (LMS); perceptual evaluation of speech quality (PESQ); selective tap filter updating; SIMO FIR model

Indexed keywords

EID: 85008018524     PISSN: 10709908     EISSN: 15582361     Source Type: Journal    
DOI: 10.1109/LSP.2009.2027658     Document Type: Article

References (16)

1. A. K. Nabelek and J. M. Picket, “Monaural and binaural speech perception through hearing aids under noise and reverberation with normal and hearing-impaired listeners,” J. Speech Hear. Res., vol. 17, pp. 724–739, 1974.
2. S. Haykin, Ed., Unsupervised Adaptive Filtering New York, Wiley, 2000, vol. II, Blind Deconvolution.
3. S. Gannot and M. Moonen, “Subspace methods for multi-microphone speech dereverberation,” EURASIP J. Appl. Signal Process., vol. 11, pp. 1074–1090, 2003.
4. T. Nakatani and M. Miyoshi, “Blind dereverberation of single channel speech signal based on harmonic structure,” in Proc. ICASSP, 2003, vol. 1, pp. 92–95.
5. M. Wu and D. L. Wang, “A two-stage algorithm for one-microphone reverberant speech enhancement,” IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp. 774–784, May 2006.
6. J.-H. Lee, S.-H. Oh, and S.-Y. Lee, “Binaural semi-blind dereverberation of noisy convoluted speech signals,” Neurocomputing, vol. 72, pp. 636–642, 2008.
7. E. A. P. Habets, “Multi-channel speech dereverberation based on a statistical model of late reverberation,” in Proc. ICASSP, 2005, vol. 4, pp. 173–176.
8. H. W. Löllmann and P. Vary, “A blind speech enhancement algorithm for the suppression of late reverberation and noise,” in Proc. ICASSP, 2009, pp. 3989–3992.
9. T. Yoshioka, T. Nakatani, T. Hikichi, and M. Miyoshi, “Maximum likelihood approach to speech enhancement for noisy reverberant signals,” in Proc. ICASSP, 2008, pp. 4585–4588.
10. Y. Huang and J. Benesty, “Adaptive multi-channel least mean square and Newton algorithms for blind channel identification,” Signal Process., vol. 82, pp. 1127–1138, 2002.
11. T. Aboulnasr and K. Mayyas, “Complexity reduction of the NLMS algorithm via selective coefficient update,” IEEE Trans. Signal Process., vol. 47, pp. 1421–1424, 1999.
12. I. Pitas, “Fast algorithms for running ordering and max/min calculation,” IEEE Trans. Circuits Syst., vol. 36, no. 6, pp. 795–804, Jun. 1989.
13. IEEE Subcommittee, “IEEE recommended practice speech quality measurements”,” IEEE Trans. Audio Electroacoust., vol. 17, no. 3, pp. 225–246, Sep. 1969.
14. B. G. Shinn-Cunningham, N. Kopco, and T. J. Martin, “Localizing nearby sound sources in a classroom: Binaural room impulse responses,” J. Acoust. Soc. Amer., vol. 117, pp. 3100–3115, 2005.
15. Perceptual Evaluation of Speech Quality (PESQ), an Objective Method for End-To-End Speech Quality Assessment of Narrow-Band Telephone Networks and Speech Coders ITU-T Recommendation, 2001, ITU-T Recommendation P.862.
16. Y. Hu and P. C. Loizou, “Evaluation of objective quality measures for speech enhancement,” IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 1, pp. 229–238, Jan. 2008.