A supervised learning approach to monaural segregation of reverberant speech

IEEE Transactions on Audio, Speech and Language Processing

Volumn 17, Issue 4, 2009, Pages 625-638

  Jin, Zhaozhang ^a   Wang, Deliang ^a  

^a The Ohio State University   (United States)

Author keywords

Computational auditory scene analysis (CASA); Monaural segregation; Room reverberation; Speech separation; Supervised learning

Indexed keywords

COMPUTATIONAL AUDITORY SCENE ANALYSIS (CASA); HARMONICITY; INVERSE FILTERING; LEARNING PROCESS; MONAURAL SEGREGATION; OBJECTIVE FUNCTIONS; POSTERIOR PROBABILITIES; REAL ENVIRONMENTS; REVERBERANT CONDITIONS; REVERBERANT ENVIRONMENTS; ROOM REVERBERATION; SEGMENTATION AND GROUPINGS; SIGNAL DEGRADATIONS; SIGNAL-TO-NOISE RATIOS; SPEECH SEGREGATIONS; SPEECH SEPARATION; SYSTEMATIC EVALUATIONS; TIME FREQUENCIES; VOICED SPEECH;

CONTINUOUS SPEECH RECOGNITION; EDUCATION; PATIENT REHABILITATION; SEGREGATION (METALLOGRAPHY); SIGNAL TO NOISE RATIO; SPEECH ANALYSIS; SPEECH PROCESSING; SUPERVISED LEARNING;

REVERBERATION;

EID: 65249103478     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2008.2010633     Document Type: Article

References (61)

1. R. Alaiz-Rodríguez, A. Guerrero-Curieses, and J. Cid-Sueiro, "Minimax classifiers based on neural networks," Pattern Recognition, vol. 38, pp. 29-39, 2005.
2. J. B. Allen and D. A. Berkley, "Image method for efficiently simulating small-room acoustics," J. Acoust. Soc. Amer., vol. 65, pp. 943-950, 1979.
3. M. C. Anzalone, L. Calandruccio, K. A. Doherty, and L. H. Carney, "Determination of the potential benefit of time-frequency gain manipulation," Ear Hear., vol. 27, pp. 480-492, 2006.
4. F. Bach and M. Jordan, "Blind one-microphone speech separation: A spectral learning approach," in Proc. NIPS, 2004, pp. 65-72.
5. R. Barandela, J. Sanchez, V. García, and E. Rangel, "Strategies for learning in class imbalance problems," Pattern Recognition, vol. 36, pp. 849-851, 2003.
6. C. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
7. P. Boersma and D. Weenink, "Praat: Doing phonetics by computer (Version 4.3.14)." 2005 [Online]. Available: http://www.fon.hum.uva.nl/ praat
8. S. F. Boll, "Supression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-27, no. 2, pp. 113-120, Apr. 1979.
9. M. S. Brandstein, "On the use of explicit speech modeling in microphone array applications," in Proc. IEEE ICASSP, 1998, pp. 3613-3616.
10. A. S. Bregman, Auditory Scene Analysis. Cambridge, MA: MIT, 1990.
11. G. Brown and M. Cooke, "Computational auditory scene analysis," Comput. Speech Lang., pp. 297-336, 1994.
12. G. J. Brown and K. J. Palomaki, "Reverberation," in Computational Auditory Scene Analysis: Principles, Algorithms, and Applications, D. L. Wang and G. J. Brown, Eds. Hoboken, NJ: Wiley/IEEE Press, 2006, pp. 209-250.
13. D. S. Brungart, P. S. Chang, B. D. Simpson, and D. L. Wang, "Isolating the energetic component of speech-on-speech masking with ideal time-frequency segregation," J. Acoust. Soc. Amer., vol. 120, pp. 4007-4018, 2006.
14. J. Chen, J. Benesty, Y. Huang, and S. Doclo, "New insights into the noise reduction Wiener filter," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 4, pp. 1218-1234, Jul. 2006.
15. M. P. Cooke, Modeling Auditory Processing and Organization. Cambridge, U.K.: Cambridge Univ. Press, 1993.
16. T. F. Cox and M. A. A. Cox, Multidimensional Scaling. New York: Chapman & Hall, 2001.
17. J. F. Culling, K. I. Hodder, and C. Y. Toh, "Effects of reverberation on perceptual segregation of competing voices," J. Acoust. Soc. Amer., vol. 114, pp. 2871-2876, 2003.
18. R. O. Duda, P. E. Hart, and D. Stock, Pattern Classification. New York: Wiley, 2001.
19. Y. Ephraim and D. Malah, "Speech enhancement using a minimum mean square error short-time spectral amplitude estimator," IEEE Trans. Acoust., Speech, Signal Process., vol. 32, pp. 1109-1121, 1984.
20. Y. Ephraim and H. L. Trees, "A signal subspace approach for speech enhancement," IEEE Trans. Speech Audio Process., vol. 3, no. 4, pp. 251-266, Jul. 1995.
21. J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, D. S. Pallett, and N. L. Dahlgren, "DARPA TIMIT Acoustic Phonetic Continuous Speech Corpus, "CDROM 1993 [Online]. Available: http://www.ldc.upenn.edu/Catalog/ LDC93S1.html
22. B. W. Gillespie, H. S. Malvar, and D. A. F. Florencio, "Speech dereverberation via maximum-kurtosis subband adaptive filtering," in Proc. IEEE ICASSP, 2001, pp. 3701-3704.
23. M. T. Hagan, H. B. Demuth, and M. H. Beale, Neural Network Design. Boston, MA: PWS Publishing, 1996.
24. G. Hu, "Monaural speech organization and segregation," Ph.D. dissertation, Biophys. Program, The Ohio State Univ., Columbus, 2006.
25. G. Hu and D. L. Wang, "Monaural speech segregation based on pitch tracking and amplitude modulation," IEEE Trans. Neural Netw., vol. 15, no. 5, pp. 1135-1150, Sep. 2004.
26. G. Hu and D. L. Wang, "An auditory scene analysis approach to monaural speech segregation," in Topics in Acoustic Echo and Noise Control, E. Hansler and G. Schmidt, Eds. New York: Springer, 2006, pp. 485-515.
27. G. Hu and D. L. Wang, "Auditory segmentation based on onset and offset analysis," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 2, pp. 396-405, Feb. 2007.
28. G. Hu and D. L. Wang, "Segregation of unvoiced speech from nonspeech interference," J. Acoust. Soc. Amer., vol. 124, pp. 1306-1319, 2008.
29. K. Hu, P. Divenyi, D. Ellis, Z. Jin, B. G. Shinn-Cunningham, and D. L. Wang, "Preliminary intelligibility tests of a monaural speech segregation system," in Proc. SAPA, 2008, pp. 11-16.
30. J. J. Jetzt, "Critical distance measurement of rooms from the sound energy spectral response," J. Acoust. Soc. Amer., vol. 65, pp. 1204-1211, 1979.
31. Z. Jin and D. L. Wang, "A supervised learning approach to monaural segregation of reverberant speech," in Proc. IEEE ICASSP, 2007, pp. 921-924.
32. M. Kukar and I. Kononenko, "Cost-sensitive learning with neural networks," in Proc. Eur. Conf. Artif. Intell., 1998, pp. 445-449.
33. H. Kuttruff, Room Acoustics. New York: Taylor & Francis, 2000.
34. S. Lawrence, I. Burns, A. Back, A. Tsoi, and C. L. Giles, "Neural network classification and unequal prior class probabilities," in Tricks of the Trade, Lecture Notes in Computer Science State-of-the-Art Surveys, G. Orr, K. R. Müller, and R. Caruana, Eds. Berlin, Germany: Springer, 1998, pp. 299-314.
35. N. Li and P. C. Loizou, "Factors influencing intelligibility of ideal binary- masked speech: Implications for noise reduction," J. Acoust. Soc. Amer., vol. 123, pp. 1673-1682, 2008.
36. J. Lim and A. Oppenheim, "All-pole modeling of degraded speech," IEEE Trans. Acoust., Speech, Signal Process., vol. 26, pp. 197-210, 1978.
37. R. Y. Litovsky, H. S. Colburn, W. A. Yost, and S. J. Guzman, "The precedence effect," J. Acoust. Soc. Amer., vol. 106, pp. 1633-1654, 1999.
38. V. Matousek, P. Mautner, J. Ocelíkova, and P. Sojka, Eds., in Proc. Text, Speech, Dialogue-Second Int. Workshop, TSD'99, Plzen, Czech Republic, September 1999, 1999, vol. 1692, Lecture Notes in Computer Science, Springer.
39. R. Meddis, "Simulation of auditory-neural transduction: Further studies," J. Acoust. Soc. Amer., vol. 83, pp. 1056-1063, 1988.
40. J. Mourjopoulos, "On the variation and invertibility of room impulse response functions," J. Sound Vibr., vol. 102, pp. 217-228, 1985.
41. H. Ney, "On the probabilistic interpretation of neural network classifiers and discriminative training criteria," IEEE Trans. Pattern Anal. Mach. Intell., vol. 17, no. 2, pp. 107-119, Feb. 1995.
42. K. J. Palomaki, G. J. Brown, and D. L. Wang, "A binaural processor for missing data speech recognition in the presence of noise and smallroom reverberation," Speech Commun., vol. 43, pp. 361-378, 2004.
43. R. D. Patterson, I. Nimmo-Smith, J. Holdsworth, and P. Rice, "An Efficient Auditory Filterbank Based on the Gammatone Function," Appl. Psychol. Unit, Cambridge, U.K., APU Rep. 2341, 1988.
44. M. H. Radfar and R. M. Dansereau, "Single-channel speech separation using soft masking filtering," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 8, pp. 2299-2310, Nov. 2007.
45. B. D. Radlovic, R. C.Williamson, and R. A. Kennedy, "Equalization in an acoustic reverberant environment: Robustness results," IEEE Trans. Speech Audio Process., vol. 8, no. 3, pp. 311-319, May 2000.
46. M. D. Richard and R. P. Lippmann, "Neural network classifiers estimate bayesian a posteriori probabilities," Neural Comput., vol. 3, pp. 461-483, 1991.
47. N. Roman, S. Srinivasan, and D. L. Wang, "Binaural segregation in multisource reverberant environments," J. Acoust. Soc. Amer., vol. 120, pp. 4040-4051, 2006.
48. N. Roman and D. L. Wang, "Pitch-based monaural segregation of reverberant speech," J. Acoust. Soc. Amer., vol. 120, pp. 458-469, 2006.
49. S. T. Roweis, "One microphone source separation," in Proc. NIPS, 2000, pp. 793-799.
50. M. Saerens, P. Latinne, and C. Decaestecker, "Adjusting a classifier for new a priori probabilities: A simple procedure," Neural Comput., vol. 14, pp. 21-41, 2002.
51. M. Sayles, B. Schouten, N. J. Ingham, and I. M. Winter, "The effect of reverberation on the temporal representation of the f0 of frequency swept harmonic complexes in the ventral cochlear nucleus," in Hearing: From Sensory Processing to Perception, B. Kollmeier, G. Klump, V. Hohmann, U. Langemann, M. Mauermann, S. Uppenkamp, and J. Verhey, Eds. Berlin, Germany: Springer, 2007, pp. 35-42.
52. S. Singh, "Multisolution estimates of classification complexity," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1534-1539, Dec. 2003.
53. N. Tatti, "Distances between data sets based on summary statistics," J. Mach. Learn. Res., vol. 8, pp. 131-154, 2007.
54. H. L. V. Trees, Detection, Estimation and Modulation Theory. New York: Wiley, 1968.
55. D. L. Wang, "On ideal binary mask as the computational goal of auditory scene analysis," in Speech Separation by Humans and Machines, P. Divenyi, Ed. Norwell, MA: Kluwer, 2005, pp. 181-197.
56. D. L. Wang and G. J. Brown, "Separation of speech from interfering sounds based on oscillatory correlation," IEEE Trans. Neural Netw., vol. 10, no. 3, pp. 684-697, May 1999.
57. D. L. Wang and G. J. Brown, Eds., Computational Auditory Scene Analysis: Principles, Algorithms and Applications. Hoboken, NJ: Wiley-IEEE Press, 2006.
58. M. Weinraub, "A theory and computational model of auditory monaural sound separation," Ph.D. dissertation, Dept. of Elect. Eng., Stanford Univ., Stanford, CA, 1985.
59. R. Weiss and D. Ellis, "Monaural speech separation using source-adapted models," in Proc. IEEE WASPAA, 2007, pp. 114-117.
60. M. Wu and D. L. Wang, "A pitch-based method for the estimation of short reverberation time," Acta Acustica United With Acustica, vol. 92, pp. 337-339, 2006.
61. 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.