Towards scaling up classification-based speech separation

IEEE Transactions on Audio, Speech and Language Processing

Volumn 21, Issue 7, 2013, Pages 1381-1390

  Wang, Yuxuan ^a   Wang, DeLiang ^a,b  

^a Ohio State University   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

Computational auditory scene analysis (CASA); deep belief networks; feature learning; monaural speech separation; support vector machines

Indexed keywords

BINARY CLASSIFICATION PROBLEMS; COMPUTATIONAL AUDITORY SCENE ANALYSIS; DEEP BELIEF NETWORKS; DISCRIMINATIVE FEATURES; FEATURE LEARNING; SEPARATION PERFORMANCE; SPEECH SEPARATION; SUPPORT VECTOR MACHINE (SVMS);

SEPARATION; SOURCE SEPARATION; SPEECH ANALYSIS;

SUPPORT VECTOR MACHINES;

EID: 84875678689     PISSN: 15587916     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASL.2013.2250961     Document Type: Article

References (47)

1. Y. Bengio, "Learning deep architectures for AI," Foundat. Trends Mach. Learn., vol. 2, no. 1, pp. 1-127, 2009.
2. P. Boersma and D. Weenink, Praat: Doing Phonetics by Computer (Version 4.3.14) 2005 [Online]. Available: http://www.fon.hum.uva.nl/praat
3. S. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Audio, Speech, Lang. Process., vol. ASSP-27, no. 2, pp. 113-120, Apr. 1979. (Pubitemid 9467471)
4. A. Bordes, S. Ertekin, J. Weston, and L. Bottou, "Fast kernel classifiers with online and active learning," J. Mach. Learn. Res., vol. 6, pp. 1579-1619, 2005.
5. L. Bottou and O. Bousquet, "The tradeoffs of large scale learning," in Adv. Neural Inf. Process. Syst. 20, 2008, pp. 161-168.
6. D. Brungart, P. Chang, B. Simpson, and D. 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. (Pubitemid 44888096)
7. C. Chang and C. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27-27, 2011.
8. H. Christensen, J. Barker, N. Ma, and P. Green, "The CHiME corpus: A resource and a challenge for computational hearing in multisource environments," in Proc. Interspeech, 2010.
9. A. Coates, H. Lee, and A. Ng, "An analysis of single-layer networks in unsupervised feature learning," in Proc. 14th Int. Conf. Artif. Intell. Statist., 2011.
10. A. Coates and A. Ng, "The importance of encoding versus training with sparse coding and vector quantization," in Proc. 28th Int. Conf. Mach. Learn., 2011.
11. G. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large vocabulary speech recognition," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 30-42, Jan. 2012.
12. Y. Ephraim and D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator," IEEE Trans. Audio, Speech, Lang. Process., vol. ASSP-32, no. 6, pp. 1109-1121, Dec. 1984.
13. D. Erhan, Y. Bengio, A. Courville, P. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?," J. Mach. Learn. Res., vol. 11, pp. 625-660, 2010.
14. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin, "LIBLINEAR: A library for large linear classification," J. Mach. Learn. Res., vol. 9, pp. 1871-1874, 2008.
15. 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, no. 3, pp. 793-830, 2009.
16. J. Garofolo, DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus. Gaithersburg, MD, USA: National Inst. of Standards and Technology, 1993.
17. K. Han and D. Wang, "A classification approach to speech segregation," J. Acoust. Soc. Amer., vol. 132, pp. 3475-3483, 2012.
18. W. Hartmann and E. Fosler-Lussier, "Investigations into the incorporation of the ideal binary mask in ASR," in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2011, pp. 4804-4807.
19. R. Hendriks, R. Heusdens, and J. Jensen, "MMSE based noise PSD tracking with low complexity," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., 2010, pp. 4266-4269.
20. G. Hinton, "Training products of experts by minimizing contrastive divergence," Neural Comput., vol. 14, no. 8, pp. 1771-1800, 2002.
21. G. Hinton, S. Osindero, and Y. Teh, "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, no. 7, pp. 1527-1554, 2006. (Pubitemid 44024729)
22. G. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-504, 2006.
23. G. Hu and D. Wang, "Segregation of unvoiced speech from nonspeech interference," J. Acoust. Soc. Amer., vol. 124, pp. 1306-1319, 2008.
24. G. Hu and D. Wang, "A tandem algorithm for pitch estimation and voiced speech segregation," IEEE Trans. Audio, Speech, Lang. Process., vol. 18, no. 8, pp. 2067-2079, Nov. 2010.
25. G. Hu, 100 nonspeech environmental sounds 2004 [Online]. Available: http://www.cse.ohio-state.edu/pnl/corpus/HuCorpus.html
26. "IEEE recommended practice for speech quality measurements," IEEE Trans. Audio Electroacoust., vol. 17, no. 3, pp. 225-246, Sep. 1969.
27. Z. Jin and D. Wang, "A supervised learning approach to monaural segregation of reverberant speech," IEEE Trans. Audio, Speech, Lang. Process., vol. 17, no. 4, pp. 625-638, May 2009.
28. Z. Jin and D. Wang, "HMM-based multipitch tracking for noisy and reverberant speech," IEEE Trans. Audio, Speech, Lang. Process., vol. 19, no. 5, pp. 1091-1102, Jul. 2011.
29. G. Kim and P. Loizou, "Improving speech intelligibility in noise using environment-optimized algorithms," IEEE Trans. Audio, Speech, Lang. Process., vol. 18, no. 8, pp. 2080-2090, Nov. 2010.
30. G. Kim, Y. Lu, Y. Hu, and P. Loizou, "An algorithm that improves speech intelligibility in noise for normal-hearing listeners," J. Acoust. Soc. Amer., vol. 126, pp. 1486-1494, 2009.
31. W. Kim and R. Stern, "Mask classification for missing-feature reconstruction for robust speech recognition with unknown background noise," Speech Commun., vol. 53, no. 1, pp. 1-11, 2011.
32. H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin, "Exploring strategies for training deep neural networks," J. Mach. Learn. Res., vol. 10, pp. 1-40, 2009.
33. H. Lee, R. Grosse, R. Ranganath, and A. Ng, "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations," in Proc. 26th Int. Conf. Mach. Learn., 2009, pp. 609-616.
34. N. Li and P. Loizou, "Factors influencing intelligibility of ideal binary-masked speech: Implications for noise reduction," J. Acoust. Soc. Amer., vol. 123, no. 3, pp. 1673-1682, 2008. (Pubitemid 351379593)
35. A. Mohamed, G. Dahl, and G. Hinton, "Acoustic modeling using deep belief networks," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 1, pp. 14-21, Jan. 2012.
36. A. Ozerov, E. Vincent, and F. Bimbot, "A general flexible framework for the handling of prior information in audio source separation," IEEE Trans. Audio, Speech, Lang. Process., vol. 20, no. 4, pp. 1118-1133, May 2012.
37. J. Platt, "Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods," Adv. Large Margin Classifiers, pp. 61-74, 1999.
38. N. Roman, D. Wang, and G. Brown, "Speech segregation based on sound localization," J. Acoust. Soc. Amer., vol. 114, pp. 2236-2252, 2003. (Pubitemid 37266649)
39. M. Seltzer, B. Raj, and R. Stern, "A Bayesian classifier for spec-trographic mask estimation for missing feature speech recognition," Speech Commun., vol. 43, no. 4, pp. 379-393, 2004.
40. S. Shalev-Shwartz, Y. Singer, and N. Srebro, "Pegasos: Primal estimated sub-gradient solver for SVM," in Proc. 24th Int. Conf. Mach. learn., 2007, pp. 807-814.
41. S. Singh, "Multiresolution estimates of classification complexity," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1534-1539, Dec. 2003.
42. N. Tatti, "Distances between data sets based on summary statistic," J. Mach. Learn. Res., vol. 8, pp. 131-154, 2007. (Pubitemid 46168465)
43. A. Varga and H. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experiment to study the effect of additive noise on speech recognition systems," Speech Commun., vol. 12, pp. 247-251, 1993.
44. D. 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, USA: Kluwer, 2005, pp. 181-197.
45. , D. Wang and G. Brown, Eds., Computational Auditory Scene Analysis: Principles, Algorithms and Applications. Hoboken, NJ, USA: Wiley-IEEE Press, 2006.
46. Y. Wang, K. Han, and D. Wang, "Exploring monaural features for classification-based speech segregation," IEEE Trans. Audio, Speech, Lang. Process., vol. 21, no. 2, pp. 270-279, Feb. 2013.
47. Y. Wang and D. Wang, "Cocktail party processing via structured prediction," in Adv. Neural Inf. Process. Syst. 25, 2012, pp. 224-232.