Dynamic noise aware training for speech enhancement based on deep neural networks

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2014, Pages 2670-2674

  Xu, Yong ^a   Du, Jun ^a   Dai, Li Rong ^a   Lee, Chin Hui ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Deep neural networks; Ideal binary mask; Noise aware training; Non stationary noise; Speech enhancement

Indexed keywords

ALGORITHMS; MEAN SQUARE ERROR; QUALITY CONTROL; SIGNAL TO NOISE RATIO; SPEECH; SPEECH COMMUNICATION;

DEEP NEURAL NETWORKS; GENERALIZATION CAPABILITY; IDEAL BINARY MASK; MINIMUM MEAN SQUARED ERROR; NONSTATIONARY NOISE; PERCEPTUAL EVALUATION OF SPEECH QUALITIES; SPEECH ENHANCEMENT METHODS; STATE-OF-THE-ART TECHNIQUES;

SPEECH ENHANCEMENT;

EID: 84910038203     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. J. Benesty, S. Makino, and J. D. Chen, Speech Enhancement, Springer, 2005.
2. S. Boll, "Suppression of acoustic noise in speech using spectral subtraction, " IEEE Trans. on Acoustic, Speech and Signal Processing, Vol. 27, No. 2, pp. 113-120, 1979.
3. J. S. Lim and A. V. Oppenheim, "Enhancement and bandwidth compression of noisy speech, " in Proc. IEEE, Vol. 67, No. 12, pp. 1586-1604, 1979.
4. Y. Ephraim and D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator, " IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. 32, No.6, pp. 1109-1121, 1984.
5. Y. Ephraim and D. Malah, "Speech enhancement using minimu mean square log spectral amplitude estimator, " IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. 33, No. 2, pp. 443- 445, 1985.
6. I. Cohen and B. Berdugo, "Speech enhancement for non- stationary noise environments, " Signal Processing, Vol. 81, No. 11, pp. 2403-2418, 2001.
7. I. Cohen, "Noise spectrum estimation in adverse environments: Improved minima controlled recursive averaging, " IEEE Trans. on Speech and Audio Processing, Vol. 11, No. 5, pp. 466-475, 2003.
8. E. A.Wan and A. T. Nelson, "Networks for speech enhancement, " in Handbook of Neural Networks for Speech Processing, Edited by Shigeru Katagiri, Artech House, Boston, 1998.
9. Y. Bengio, "Learning deep architectures for AI, " Foundations and Trends in Machine Learning, Vol. 2, No. 1, pp. 1-127, 2009.
10. G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, Vol. 313, No. 5786, pp. 504-507, 2006.
11. A. L. Maas, T. M. O'Neil, A. Y. Hannun and A. Y. Ng, "Recurrent neural network feature enhancement: The 2nd CHiME challenge, " In Proceedings The 2nd CHiME Workshop on Machine Listening in Multisource Environments held in conjunction with ICASSP, pp. 79-80, 2013.
12. A. L. Maas, Q. V. Le, T. M. O'Neil, O. Vinyals, P. Nguyen and A. Y. Ng, "Recurrent Neural Networks for Noise Reduction in Robust ASR, " Proc. Interspeech, pp. 22-25, 2012.
13. Y. Xu, J. Du, L.-R. Dai and C.-H. Lee, "An experimental study on speech enhancement based on deep neural networks, " IEEE Signal Processing Letters, Vol. 21, No. 1, pp. 65-68, 2014.
14. B.-Y. Xia and C.-C. Bao, "Wiener filtering based speech enhancement with weighted denoising auto-encoder and noise classification, " Speech Communication, Vol. 60, pp. 13-29, 2014.
15. B.-Y. Xia and C.-C. Bao, "Speech enhancement with weighted de- noising Auto-Encoder, " Proc. Interspeech, pp. 3444-3448, 2013.
16. X.-G. Lu and Y. Tsao and S. Matsuda and C. Hori, "Speech enhancement based on deep denoising Auto-Encoder, " Proc. Inter- speech, pp. 436-440, 2013.
17. Y. X. Wang and D. L. Wang, "Towards scaling up classification- based speech separation, " IEEE Trans. on Audio, Speech and Lan- guage Processing, Vol. 21, No. 7, pp. 1381-1390, 2013.
18. D. L. Wang and G. Brown, Eds., Computational Auditory Scene Analysis: Principles, Algorithms and Applications. Hoboken, NJ, USA: Wiley-IEEE Press, 2006.
19. S. Rangachari and P. C. Loizou, "A noise-estimation algorithm for highly non-stationary environments, " Speech Communication, Vol. 48, No. 2, pp. 220-231, 2006.
20. M. Seltzer, D. Yu and Y. Wang, "An investigation of deep neural networks for noise robust speech recognition, " Proc. ICASSP, pp. 7398-7402, 2013.
21. O. Abdel-Hamid and H. Jiang, "Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminative learning of speaker code, " Proc. ICASSP, pp. 7942-7946, 2013.
22. T. Gerkmann, R. C. Hendriks, "Unbiased MMSE-based noise power estimation with low complexity and low tracking delay, " IEEE Trans. on Audio, Speech, and Language Processing, Vol. 20, No. 4, pp. 1383-1393, 2012.
23. R. C. Hendriks, R. Heusdens, and J. Jensen, "MMSE based noise PSD tracking with low complexity, " Proc. ICASSP, pp. 4266- 4269, 2010.
24. J. H. Hansen, V. Radhakrishnan and K. H. Arehart, "Speech enhancement based on generalized minimum mean square error estimators and masking properties of the auditory system, " IEEE trans. on Audio, Speech and Language Processing, Vol. 14, No. 6, pp. 2049-2063, 2006.
25. A. Varga and H. J. M. Steeneken, "Assessment for automatic speech recognition: II. NOISEX-92: A database and an experi- ment to study the effect of additive noise on speech recognition systems, " Speech Communication, Vol. 12, No. 3, pp. 247-251, 1993.
26. J. Du and Q. Huo, "A speech enhancement approach using piece- wise linear approximation of an explicit model of environmental distortions, " Proc. Interspeech, pp. 569-572, 2008.
27. L. Deng, M. L. Seltzer, D. Yu, A. Acero, A. R. Mohamed and G. E. Hinton. "Binary coding of speech spectrograms using a deep auto-encoder, " Proc. Interspeech, pp. 1692-1695, 2010.
28. Y. X. Wang, A. Narayanan and D. L. Wang, "On training targets for supervised speech separation, " Technical Report OSU-CISRC- 2/14-TR05, Department of Computer Science and Engineering, The Ohio State University, 2014.
29. Y. X. Wang, K. Han and D. L. Wang, "Exploring monaural features for classification-based speech segregation, " IEEE Trans. on Audio, Speech, and Language Processing, Vo. 21, No. 2, pp. 270- 279, 2013.
30. H. G. Hirsch and D. Pearce, "The AURORA experimental frame- work for the preformance evaluations of speech recognition systems under noisy conditions, " Proc. ISCA ITRW ASR, pp. 181- 188, 2000.
31. J. S. Garofolo, Getting started with the DARPA TIMIT CD-ROM: An acoustic phonetic continuous speech database, NIST Tech Report, 1988.
32. ITU-T, Recommendation P.862, "Perceptual evaluation of speech quality (PESQ): An objective method for end-to-end speech quality assessment of narrow-band telephone network- s and speech codecs, " International Telecommunication Union- Telecommunication Standardisation Sector, 2001.
33. G. Hu, 100 nonspeech environmental sounds, 2004. http://www.cse.ohio-state.edu/pnl/corpus/HuCorpus.html.