Monaural speech separation based on gain adapted minimum mean square error estimation

Journal of Signal Processing Systems

Volumn 61, Issue 1, 2010, Pages 21-37

  Radfar, M H ^a,b   Dansereau, R M ^a   Chan, W Y ^b  

^a CARLETON UNIVERSITY   (Canada)

^b QUEEN S UNIVERSITY   (Canada)

Author keywords

Gain adaptation; Minimum mean square error estimation; Mixmax approximation; Model based monaural speech separation; Source separation

Indexed keywords

GAIN ADAPTATION; MINIMUM MEAN SQUARE ERROR ESTIMATIONS; MIXMAX APPROXIMATION; MODEL-BASED MONAURAL SPEECH SEPARATION; SOURCE SEPARATION;

ESTIMATION; MAXIMUM LIKELIHOOD ESTIMATION; MEAN SQUARE ERROR; SPEECH ANALYSIS;

SEPARATION;

EID: 80052817343     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-008-0274-7     Document Type: Conference Paper

References (63)

1. Camastra, F., & Vinciarelli, A. (2007). Machine learning for audio, image and video analysis: Theory and applications (advanced information and knowledge processing). New York: Springer.
2. Wang, D., & Brown, G. J. (Eds.) (2006). Computational auditory scene analysis: Principles, algorithms, and applications. New York: IEEE/Wiley-Interscience.
3. Ellis, D. (2006). Model-based scene analysis. In D. Wang & G. Brown (Eds.), Computational auditory scene analysis: Principles, algorithms, and applications. New York: Wiley/ IEEE Press.
4. Roweis, S. (2000). One microphone source separation. In Proc. Neural Inf. Process. Syst. (pp. 793-799).
5. Rowies, S. T. (2003). Factorial models and refiltering for speech separation and denoising. In EUROSPEECH-03 (Vol. 7, pp. 1009-1012), May.
6. Radfar, M. H., & Dansereau, R. M. (2007). Single channel speech separation using soft mask filtering. IEEE Transactions on Audio, Speech and Language Processing, 25(8), 2299-2310, Nov.
7. Radfar, M. H., & Dansereau, R. M. (2007). Long-term gain estimation in model-based single channel speech separation. In Proc. IEEE workshop on applications of signal processing to audio and acoustics (WASPAA2007). New Paltz, New York, October.
8. Radfar, M. H., & Dansereau, R. M. (2007). Single channel speech separation using minimum mean square error estimation of sources' log spectra. In Proc. IEEE international workshop on machine learning for signal processing (MLSP'2007 Thessalonike, Greece), Aug.
9. Radfar, M. H., Dansereau, R. M., & Sayadiyan, A. (2006). Performance evaluation of three features for modelbased single channel speech separation problem. In Interspeech 2006, Intern. Conf on Spoken Language Processing (ICSLP'2006 Pittsburgh, USA) (pp. 17-21), Sept.
10. Radfar, M. H., & Dansereau, R. M. (2007). Single channel speech separation using maximum a posteriori estimation. In Proc. international conference on spoken language processing (Interspeech- ICSLP 07). Antwerp, Belgium, Aug.
11. Schmidt, M. N., & Olsson, R. K. (2007). Linear regression on sparse features for single-channel speech separation. In Proc. IEEE workshop on applications of signal processing to audio and acoustics (WASPAA2007) (pp. 26-29). New Paltz, New York, October.
12. Reddy, A. M., & Raj, B. (2007). Soft mask methods for singlechannel speaker separation. Audio, Speech and Language Processing, IEEE Transactions on, 25(6), 1766-1776, Aug.
13. Weiss, R., & Ellis, D. (2006). Estimating single-channel source separation masks: Relevance vector machine classifiers vs. pitch-based masking. In Proc. workshop on statistical and perceptual audition SAPA-06 (pp. 31-36), Oct.
14. Beierholm, T., Pedersen, B. D., & Winther, O. (2004). Low complexity Bayesian single channel source separation. In Proc. ICASSP-04 (Vol. 5, pp. 529-532), May.
15. Kristjansson, T., Attias, T. H., & Hershey, J. (2004). Single microphone source separation using high resolution signal reconstruction. In Proc. ICASSP-04 (pp. 817-820), May.
16. Radfar, M. H., Dansereau, R. M., & Sayadiyan (2007). A maximum likelihood estimation of vocal-tract-related filter characteristics for single channel speech separation. EURASIP Journal on Audio, Speech, and Music Processing, 2007, 15, 84186. doi: 10.1155/2007/84186.
17. Radfar, M. H., Dansereau, R. M., & Sayadiyan, A. (2007). Monaural speech segregation based on fusion of sourcedriven with model-driven techniques. Speech Communication, 49(6), 464-476, June. (Pubitemid 46891624)
18. Reyes-Gomez, M. J., Ellis, D., & Jojic, N. (2004). Multiband audio modeling for single channel acoustic source separation. In Proc. ICASSP-04 (Vol. 5, pp. 641-644), May.
19. Reddy, A. M., & Raj, B. (2004). A minimum mean squared error estimator for single channel speaker separation. In INTERSPEECH-2004 (pp. 2445-2448), Oct.
20. Brown, G. J., & Wang, D. L. (2005). Separation of speech by computational auditory scene analysis. In Speech enhancement (pp. 371-402). New York: Springer.
21. Brown, G. J., & Cooke, M. (1994). Auditory scene analysis. Computer Speech and Language, 8(4), 297-336.
22. Cooke, M., & Ellis, D. P. W. (2001). The auditory organization of speech and other sources in listeners and computational models. Speech Communication, 35(3), 141-177, October. (Pubitemid 32922990)
23. Ellis, D. P. W. (1999). Using knowledge to organize sound: The prediction-driven approach to computational auditory scene analysis and its application to speech/nonspeech mixtures. Speech Communication, 27(3), 281-298, April.
24. Nakatani, T., & Okuno, H. G. (1999). Harmonic sound stream segregation using localization and its application to speech stream segregation. Speech Communication, 27(3), 209-222, April.
25. Brown, G. J., & Wang, D. L. (2005). Separation of speech by computational auditory scene analysis. In J. Benesty, S. Makino, & J. Chen (Eds.), Speech enhancement (pp. 371-402). New York: Springer.
26. Darwin, C. J., & Carlyon, R. P. (1995). Auditory grouping. In B. C. J. Moore (Ed.), The handbook of perception and cognition (Vol. 6, chapter Hearing, pp. 387-424). London: Academic.
27. Wang, D. L., & Brown, G. J. (1999). Separation of speech from interfering sounds based on oscillatory correlation. IEEE Transactions on Neural Networks, 10, 684-697, May.
28. Hu, G., & Wang, D. L. (2004). Monaural speech segregation based on pitch tracking and amplitude modulation. IEEE Transactions on Neural Networks, 25(5), 1135-1150, Sept.
29. Bregman, A. S. (1994). Computatinal auditory scene analysis. Cambridge: MIT.
30. Li, Y., Amari, S., Cichocki, A., Ho, D. W. C., & Shengli, X. (2006). Underdetermined blind source separation based on sparse representation. IEEE Transactions on Speech Audio Processing, 54(2), 423-437, Feb. (Pubitemid 43141892)
31. Theis, F. J., Puntonet, C. G., & Lang, E. W. (2006). Medianbased clustering for underdetermined blind signal processing. IEEE Signal Processing Letters, 13(2), 96-99, Feb. (Pubitemid 43137981)
32. Bofill, P., & Zibulevsky, M. (2001). Underdetermined blind source separation using sparse representations. Signal Process, 81, 2353-2362. (Pubitemid 32943462)
33. Jutten, C., & Herault, J. (1991). Blind separation of sources, Part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1-10. (Pubitemid 21679270)
34. Common, P. (1994). Independent component analysis, a new concept? Signal Processing, 36, 287-314.
35. Bell, A. J., & Sejnowski, T. J. (1995). An informationmaximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
36. Amari, S. I., & Cardoso, J. F. (1997). Blind source separationsemiparametric statistical approach. IEEE Transactions on Signal Processing, 45(11), 2692-2700.
37. Luo, Y., Wang, W., Chambers, J. A., Lambotharan, S., & Proudler, I. K. (2006). Exploitation of source non-stationarity in underdetermined blind source separation with advanced clustering techniques. IEEE Transactions Signal Processing, 54(6), 2198-2212, June. (Pubitemid 43811413)
38. Lewicki, M. S., & Sejnowski, T. J. (1998). Learning nonlinear overcomplete representations for efficient coding. In M. I. Jordan, M. J. Kearns, & S. A. Solla (Eds.), Advances in neural information processing systems (Vol. 10). Cambridge: MIT.
39. Schmidt, M. N., & Olsson, R. K. (2006). Single-channel speech separation using sparse non-negative matrix factorization. In Proc. Interspeech 2006, Intern. Conf on Spoken Language Processing (ICSLP'2006 Pittsburgh), Sept.
40. Virtanen, T. (2003). Sound source separation using sparse coding with temporal continuity objective. In Proc. Int. Comput. Music Conference (pp. 231-234).
41. Jang, G. J., & Lee, T. W. (2003). A probabilistic approach to single channel source separation. In Proc. Advances in Neural Inform. Process. Systems (pp. 1173-1180).
42. Radfar, M. H., Banihashemi, A. H., Dansereau, R. M., & Sayadiyan, A. (2006). A non-linear minimum mean square error estimator for the mixture-maximization approximation. Electronic Letters, 42(12), 75-76, June.
43. Ephraim, Y. (1992). Gain-adapted hidden markov models for recognition of clean andnoisy speech. IEEE Transactions on Audio, Speech and Language Processing, 40(6), 1303-1316, Jun.
44. Zhao, D. Y., & Kleijn, W. B. (2007). Hmm-based gain modeling for enhancement of speech in noise. IEEE Transactions on Audio, Speech and Language Processing, 25(3), 882-892, March.
45. Benaroya, L., Bimbot, F., & Gribonval, R. (2006). Audio source separation with a single sensor. IEEE Transactions on Speech Audio Processing, 14(1), 191-199, Jan. (Pubitemid 43863465)
46. Parsons, T. W. (1976). Separation of speech from interfering speech by means of harmonic selection. Journal of the Acoustical Society of America, 60, 911-918, Aug.
47. Kameoka, H., Nishimoto, T., & Sagayama, S. (2004). Multipitch trajectory estimation of concurrent speech based on harmonic GMM and nonlinear Kalman filtering. In INTERSPEECH-2004 (Vol. 1, pp. 2433-2436), Oct.
48. De Cheveigné, A., & Kawahara, H. (1999). Multiple period estimation and pitch perception model. Speech Communication, 27, 175-185, April.
49. Kwon, Y. H., Park, D. J., & Ihm, B. C. (2000). Simplified pitch detection algorithm of mixed speech signals. In Proc. ISCAS83 (Vol. 3, pp. 722-725), May.
50. Wu, M., Wang, D. L., & Brown, G. J. (2003). A multipitch tracking algorithm for noisy speech. IEEE Transactions on Acoustics, Speech, and Signal Process, 22(3), 229-241, May.
51. Tolonen, D., & Karjalainen, M. (2000). A computationally efficient multipitch analysis model. IEEE Transactions on Acoustics, Speech, and Signal Process, 8, 708-716, Nov.
52. Chazan, D., Stettiner, Y., & Malah, D. (1993). Optimal multi-pitch estimation using the EM algorithm for co-channel speech separation. In Proc. ICASSP-93 (pp. 728-731), April.
53. Weintraub, M. (1986). A computational model for separating two simultaneous talkers. In Proc. ICASSP-86 (Vol. 11, pp. 81-84), April.
54. Hanson, B. A., & Wong, D. Y. (1984). The harmonic magnitude suppression (HMS) technique for intelligibility enhancement in the presence of interfering speech. In Proc. ICASSP-84 (Vol. 9, pp. 65-68), Mar.
55. Morgan, D. P., George, E. B., Lee, L. T., & Key, S. M. (1997). Cochannel speaker separation by harmonic enhancment and suppression. IEEE Transactions in Acoustics, Speech, and Signal Process, 5(5), 407-424, Sept. (Pubitemid 127746014)
56. Kanjilal, P. P., & Palit, S. (1994). Extraction of multiple periodic waveforms from noisy data. In Proc. ICASSP-94 ( Vol. 2, pp. 361-364), April.
57. ? 57. Epharaim, Y., & Merhav, N. (1992). Lower and upper bounds on the minimum mean-square error in composite source signal estimation. IEEE Transactions on Information Theory, 38(6), 1709-1724, Nov.
58. Nadas, A., Nahamoo, D., & Picheny, M. A. (1989). Speech recognition using noise-adaptive prototypes. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(10), 1495-1503, Oct. (Pubitemid 20617876)
59. Papoulis, A. (1991). Probability, random variables, and stochastic processes. New York: McGraw-Hill.
60. Bradie, B. (2006). A friendly introduciton to numerical analysis. Englewood Cliffs: Pearson Prentice Hall.
61. Rabiner, L. R., & Schafer, R. W. (1978). Digital processing of speech signals. Englewood Cliffs: Prentice-Hall.
62. Cooke, M. P., Barker, J., Cunningham, S. P., & Shao, X. (2005). An audio-visual corpus for speech perception and automatic speech recognition. JASA, Nov.
63. Spiegel, M. R. (1998). Schaum's mathematical handbook of formulas and tables (2nd edn). New York: McGraw-Hill, June.