Experiments on deep learning for speech denoising

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

Volumn , Issue , 2014, Pages 2685-2689

  Liu, Ding ^a   Smaragdis, Paris ^a,b   Kim, Minje ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b ADOBE RESEARCH   (United States)

Author keywords

Deep learning; Neural networks; Source separation; Speech denoising

Indexed keywords

EXPERIMENTS; NEURAL NETWORKS; SIGNAL TO NOISE RATIO; SOURCE SEPARATION; SPEECH; SPEECH RECOGNITION;

CLEAN SPEECH; DE-NOISED SIGNALS; DEEP LEARNING; NOISE TYPES; NOISY SPEECH; PARAMETER CHOICE; SINGLE-CHANNEL; SPEECH DENOISING;

SPEECH COMMUNICATION;

EID: 84910049527     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. S. Boll, "Suppression of acoustic noise in speech using spectral subtraction, " Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 27, no. 2, pp. 113- 120, 1979.
2. Y. Ephraim and D. Malah, "Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator, " Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 32, no. 6, pp. 1109-1121, 1984.
3. P. Scalart et al., "Speech enhancement based on a priori signal to noise estimation, " in Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE International Conference on, vol. 2. IEEE, 1996, pp. 629-632.
4. Y. Ephraim and H. L. Van Trees, "A signal subspace approach for speech enhancement, " Speech and Audio Processing, IEEE Transactions on, vol. 3, no. 4, pp. 251-266, 1995.
5. K. W. Wilson, B. Raj, P. Smaragdis, and A. Divakaran, "Speech denoising using nonnegative matrix factorization with priors." in ICASSP, 2008, pp. 4029-4032.
6. E. A. Wan and A. T. Nelson, "Networks for speech enhancement, " Handbook of neural networks for speech processing. Artech House, Boston, USA, 1999.
7. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al., "Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, " Signal Processing Magazine, IEEE, vol. 29, no. 6, pp. 82-97, 2012.
8. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, "Deep learning for monaural speech separation, " in ICASSP, 2014 In Press.
9. Y. Xu, J. Du, L. Dai, and C. Lee, "An experimental study on speech enhancement based on deep neural networks, " IEEE Signal Processing Letters, vol. 21, no. 1, 2014.
10. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol, "Extracting and composing robust features with denoising autoencoders, " in Proceedings of the 25th international conference on Machine learning. ACM, 2008, pp. 1096-1103.
11. S. S. Haykin, S. S. Haykin, S. S. Haykin, and S. S. Haykin, Neural networks and learning machines. Pearson Education Upper Saddle River, 2009, vol. 3.
12. D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio, "Why does unsupervised pre-training help deep learning?" The Journal of Machine Learning Research, vol. 11, pp. 625-660, 2010.
13. M. Riedmiller and H. Braun, "A direct adaptive method for faster back propagation learning: The rprop algorithm, " in IEEE International Conference on Neural Networks, 1993, pp. 586-591.
14. D. Nguyen and B.Widrow, "Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights, " in Neural Networks, 1990., 1990 IJCNN International Joint Conference on, June 1990, pp. 21-26 vol.3.
15. C. Févotte, R. Gribonval, and E. Vincent, "Bss eval, a toolbox for performance measurement in (blind) source separation." [Online]. Available: Http://bassdb.gforge.inria.fr/bsseval.
16. C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, "An algorithm for intelligibility prediction of time- frequency weighted noisy speech, " Audio, Speech, and Language Processing, IEEE Transactions on, vol. 19, no. 7, pp. 2125-2136, 2011.