Speech separation by cost-sensitive deep learning

Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017

Volumn 2018-February, Issue , 2017, Pages 159-162

  Zhang, Xiao Lei ^a  

^a NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

SEPARATION; SIGNAL TO NOISE RATIO; SOURCE SEPARATION;

ADVERSE ENVIRONMENT; COST-SENSITIVE; GENERALISATION; LEARNING METHODS; LOW-SNR ENVIRONMENT; MULTI-CONDITION TRAININGS; PERFORMANCE; SPEECH SEPARATION; TRAINING COSTS; TRAINING DATA;

DEEP LEARNING;

EID: 85047735014     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/APSIPA.2017.8282020     Document Type: Conference Paper

References (14)

1. Y. Wang and D. L. Wang, "Towards scaling up classification-based speech separation," IEEE TASLP, vol. 21, no. 7, pp. 1381-1390, 2013.
2. Y. Wang, A. Narayanan, and D. L. Wang, "On training targets for supervised speech separation," IEEE/ACM TASLP, vol. 22, no. 12, pp. 1849-1858, 2014.
3. J. Chen, Y. Wang, and D. L. Wang, "A feature study for classificationbased speech separation at very low signal-to-noise ratio," IEEE/ACM Trans. Audio, Speech, Lang. Process., vol. 22, no. 12, pp. 1993-2002, 2014.
4. Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, "An experimental study on speech enhancement based on deep neural networks," IEEE SPL, vol. 21, no. 1, pp. 65-68, 2014.
5. F. Weninger, J. R. Hershey, J. Le Roux, and B. Schuller, "Discriminatively trained recurrent neural networks for single-channel speech separation," in GlobalSIP, 2014, pp. 577-581.
6. Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, "A regression approach to speech enhancement based on deep neural networks," IEEE/ACM TASLP, vol. 23, no. 1, pp. 7-19, 2015.
7. P.-S. Huang, M. Kim, M. Hasegawa-Johnson, and P. Smaragdis, "Joint optimization of masks and deep recurrent neural networks for monaural source separation," IEEE/ACM TASLP, vol. 23, no. 12, pp. 2136-2147, 2015.
8. D. S. Williamson, Y. Wang, and D. L. Wang, "Complex ratio masking for monaural speech separation," IEEE/ACM TASLP, vol. 24, no. 3, pp. 483-492, 2016.
9. X.-L. Zhang and D. L. Wang, "A deep ensemble learning method for monaural speech separation," IEEE/ACM TASLP, vol. 24, no. 5, pp. 967-977, 2016.
10. D. L. Wang and J. Chen, "Supervised speech separation based on deep learning: An overview," arXiv preprint arXiv:1708.07524, 2017.
11. Y. Wang, J. Chen, and D. L. Wang, "Deep neural network based supervised speech segregation generalizes to novel noises through largescale training," Tech. Rep. OSU-CISRC-3/15-TR02, Dept. of Comput. Sci., Eng., The Ohio State Univ., Columbus, OH, USA, 2015.
12. X.-L. Zhang and D. L. Wang, "Boosting contextual information for deep neural network based voice activity detection," IEEE/ACM TASLP, vol. 24, no. 2, pp. 252-264, 2016.
13. Z.-H. Zhou and X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem," IEEE TKDE, vol. 18, no. 1, pp. 63-77, 2006.
14. M. Cooke and T.-W. Lee, "Speech separation challenge," http://staffwww.dcs.shef.ac.uk/people/M.Cooke/SpeechSeparationChallenge. htm, 2006.