Nonnegative HMM for babble noise derived from speech HMM: Application to speech enhancement

IEEE Transactions on Audio, Speech and Language Processing

Volumn 21, Issue 5, 2013, Pages 998-1011

  Mohammadiha, Nasser ^a   Leijon, Arne ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

Babble noise; hidden Markov model; nonnegative matrix factorization; speech enhancement

Indexed keywords

BABBLE NOISE; BASIS MATRIX; BASIS VECTOR; COCKTAIL PARTY; CONVENTIONAL METHODS; EXPECTATION-MAXIMIZATION ALGORITHMS; GAIN PARAMETER; MULTI-TALKER BABBLE; NOISE REDUCTION ALGORITHMS; NON NEGATIVES; NONNEGATIVE MATRIX FACTORIZATION; POWER-SPECTRA; PROCESSING ALGORITHMS; RECURSIVE EM; SPARSE NON-NEGATIVE MATRIX FACTORIZATIONS; SPARSITY CONSTRAINTS; SPEECH SIGNALS; SPEECH WAVEFORMS; STATIONARY MODELS; TIME VARYING PARAMETER; WAVE FORMS;

HIDDEN MARKOV MODELS; MATRIX ALGEBRA; SPEECH; SPEECH ENHANCEMENT;

ALGORITHMS;

EID: 84873897366     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2013.2243435     Document Type: Article

References (46)

1. E. Cherry, "Some experiments on the recognition of speech, with one and two ears," J. Acoust. Soc. Amer. (JASA), vol. 25, pp. 975-979, 1953.
2. B. Arons, "A review of the cocktail party effect," J. Acoust. Soc. Amer. (JASA), vol. 12, pp. 35-50, 1992.
3. S. Haykin and Z. Chen, "The cocktail party problem," Neural Comput., vol. 17, pp. 1875-1902, 2005.
4. S. A. Simpson and M. Cooke, "Consonant identification in N-talker babble is a nonmonotonic function of N," J. Acoust. Soc. Amer. (JASA), vol. 118, no. 5, pp. 2775-2778, 2005.
5. N. Krishnamurthy and J. Hansen, "Babble noise: Modeling, analysis, and applications," IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 7, pp. 1394-1407, Sep. 2009.
6. Y. Ephraim and D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator," IEEE Trans. Audio, Speech, Signal Process., vol. ASSP-32, no. 6, pp. 1109-1121, Dec. 1984.
7. X. Shen and L. Deng, "A dynamic system approach to speech enhancement using the filtering algorithm," IEEE Trans. Speech Audio Process., vol. 7, no. 4, pp. 391-399, Jul. 1999.
8. J. Vermaak, C. Andrieu, A. Doucet, and S. Godsill, "Particle Methods for Bayesian Modeling and Enhancement of Speech Signals," IEEE Trans. Speech Audio Process., vol. 10, no. 3, pp. 173-185, Mar. 2002.
9. R. Martin, "Speech enhancement based on minimum mean-square error estimation and supergaussian priors," IEEE Trans. Speech Audio Process., vol. 13, no. 5, pp. 845-856, Sep. 2005.
10. V. Grancharov and J. S. B. Kleijn, "On causal algorithms for speech enhancement," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 3, pp. 764-773, May 2006.
11. J. S. Erkelens, R. C. Hendriks, R. Heusdens, and J. Jensen, "Minimum Mean-Square Error estimation of discrete Fourier coefficients with generalized Gamma priors," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 6, pp. 1741-1752, Aug. 2007.
12. H. Levitt, "Noise reduction in hearing aids: An overview," J. Rehab. Res. Develop., vol. 38, pp. 111-121, 2001.
13. S. Srinivasan, J. Samuelsson, and W. Kleijn, "Codebook driven short-term predictor parameter estimation for speech enhancement," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 163-176, Jan. 2006.
14. Y. Ephraim, "A Bayesian estimation approach for speech enhancement using hidden Markov models," IEEE Trans. Signal Process., vol. 40, no. 4, pp. 725-735, Apr. 1992.
15. H. Sameti, H. Sheikhzadeh, L. Deng, and R. Brennan, "HMM-based strategies for enhancement of speech signals embedded in nonsta-tionary noise," IEEE Trans. Speech Audio Process., vol. 6, no. 5, pp. 445-455, Sep. 1998.
16. D. Y. Zhao and W. B. Kleijn, "HMM-based gain modeling for enhancement of speech in noise," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 3, pp. 882-892, Mar. 2007.
17. D. D. Lee and H. S. Seung, "Learning the parts of objects by non-negative matrix factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999.
18. P. Smaragdis, "Convolutive speech bases and their application to supervised speech separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 15, no. 1, pp. 1-12, Jan. 2007.
19. C. Févotte, N. Bertin, and J. L. Durrieu, "Nonnegative matrix factorization with the Itakura-Saito divergence: With application to music analysis," Neural Comput., vol. 21, pp. 793-830, 2009.
20. A. Ozerov and C. Févotte, "Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 18, no. 3, pp. 550-563, Mar. 2010.
21. K. W. Wilson, B. Raj, P. Smaragdis, and A. Divakaran, "Speech de-noising using nonnegative matrix factorization with priors," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2008, pp. 4029-4032.
22. G. J. Mysore and P. Smaragdis, "A non-negative approach to semi-supervised separation of speech from noise with the use of temporal dynamics," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process. (ICASSP), May 2011, pp. 17-20.
23. N. Mohammadiha, T. Gerkmann, and A. Leijon, "A new approach for speech enhancement based on a constrained nonnegative matrix factorization," in Proc. IEEE Int. Symp. Intell. Signal Process. and Commun. Syst. (ISPACS), 2011, pp. 1-5.
24. N. Mohammadiha, T. Gerkmann, and A. Leijon, "A new linear MMSE filter for single channel speech enhancement based on nonnegative matrix factorization," in Proc. IEEE Workshop Applicat. Signal Process. Audio Acoust. (WASPAA), 2011, pp. 45-48.
25. N. Mohammadiha, J. Taghia, and A. Leijon, "Single channel speech enhancement using Bayesian NMF with recursive temporal updates of prior distributions," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process. (ICASSP), 2012, pp. 4561-4564.
26. D. M. Titterington, "Recursive parameter estimation using incomplete data," J. R. Statist. Soc. Ser. B (Methodological), vol. 46, pp. 257-267, 1984.
27. V. Krishnamurthy and J. Moore, "On-line estimation of hidden Markov model parameters based on the Kullback-Leibler information measure," IEEE Trans. Signal Process., vol. 41, no. 8, pp. 2557-2573, Aug. 1993.
28. L. Rabiner, "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE, vol. 77, no. 2, pp. 257-286, Feb. 1989.
29. O. Cappé, E. Moulines, and T. Ryden, Inference in Hidden Markov Models, ser. Springer Series in Statistics. New York, NY, USA: Springer, 2005.
30. N. Mohammadiha, R. Martin, and A. Leijon, "Spectral domain speech enhancement using HMM state-dependent super-Gaussian priors," IEEE Signal Process. Lett., vol. 20, no. 3, pp. 253-256, Mar. 2013.
31. I. Cohen, "Speech spectral modeling and enhancement based on autoregressive conditional heteroscedasticity models," Signal Process., vol. 86, no. 4, pp. 698-709, Apr. 2006.
32. C. M. Bishop, Pattern Recognition and Machine Learning. New York, NY, USA: Springer-Verlag, 2006.
33. J. A. Bilmes, "A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models," Univ. of California, Berkeley, CA, USA, 1997, Tech. Rep. ICSI-TR-97-021.
34. S. E. Levinson, "Continuously variable duration hidden Markov models for automatic speech recognition," Comput. Speech Lang., vol. 1, pp. 29-45, 1986.
35. A. L. Yuille and A. Rangarajan, "The concave-convex procedure," Neural Comput., vol. 15, pp. 915-936, 2003.
36. B. K. Sriperumbudur and G. R. G. Lanckriet, "On the convergence of the concave-convex procedure," in Proc. Adv. in Neural Inf. Process. Syst., 2009.
37. S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge, U.K.: Cambridge Univ. Press, 2004.
38. E. Weinstein, M. Feder, and A. Oppenheim, "Sequential algorithms for parameter estimation based on the Kullback-Leibler information measure," IEEE Trans. Acoust., Speech, Signal Process., vol. 38, no. 9, pp. 1652-1654, Sep. 1990.
39. "Speech processing, transmission and quality aspects (STQ), distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms," 2007, Tech. Rep. ETSI ES 202 050 V1.1.5.
40. E. Vincent, R. Gribonval, and C. Févotte, "Performance measurement in blind audio source separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 4, pp. 1462-1469, Apr. 2006.
41. P. C. Loizou, Speech Enhancement: Theory and Practice. Boca Raton, FL, USA: CRC, 2007.
42. K. K. Paliwal and W. B. Kleijn, "Quantization of LPC parameters," in Speech Coding Synth., W. Kleijn and K. Paliwal, Eds. New York, NY, USA: Elsevier, 1995, ch. 12, pp. 443-466.
43. "Perceptual evaluation of speech quality (PESQ), and objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs," 2000, Tech. Rep.
44. T. Lotter and P. Vary, "Speech enhancement by MAP spectral amplitude estimation using a super-Gaussian speech model," EURASIP J. Appl. Signal Process., vol. 2005, pp. 1110-1126, 2005.
45. "Method for the subjective assessment of intermediate quality level of coding systems," ITU-R Rec. BS.1534-1 Std, 2001-2003 [Online]. Available: http://www.itu.int
46. T. Kawamura and K. Iwase, "Characterizations of the distributions of power inverse Gaussian and others based on the entropy maximization principle," J. Jpn. Statist. Soc., vol. 33, no. 1, pp. 95-104, 2003.