A sparse non-parametric approach for single channel separation of known sounds

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1705-1713

  Smaragdis, Paris ^a   Shashanka, Madhusudana ^b   Raj, Bhiksha ^c  

^a ADOBE RESEARCH   (United States)

^b MARS INC   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

Example based representation; Signal separation; Sparse models

Indexed keywords

SEPARATION;

EXAMPLE-BASED REPRESENTATIONS; LEARN+; NONPARAMETRIC APPROACHES; SIGNAL SEPARATION; SINGLE CHANNEL SEPARATION; SPARSE MODELS; STATISTIC MODELING; TRAINING DATA;

MIXTURES;

EID: 84858719009     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. S. T. Roweis, One microphone source separation, in Advances in Neural Information Processing Systems, 2001.
2. Reddy, A.M. and B. Raj. Soft Mask Methods for Single-Channel Speaker Separation, in IEEE Transactions of Audio, Speech, and Language Processing, Volume: 15, Issue: 6, Aug 2007.
3. T. Kristjansson, J. Hershey, P. Olsen, S. Rennie, and R. Gopinath, Super-human multi-talker speech recognition: The IBM 2006 speech separation challenge system, in International Conference on Spoken Language Processing (INTERSPEECH), 2006, pp. 97-100, Kluwer Academic Publishers, ch. 20, pp. 295304.
4. Casey, M.A., and A. Westner. Separation of mixed audio sources by independent subspace analysis, in Proceedings of the International Conference of Computer Music, 2000.
5. Jang, G.-J., T.-W. Lee. A Maximum Likelihood Approach to Single-channel Source Separation, in Journal of Machine Learning Research 4 (2003) pp. 1365-1392.
6. Pearlmutter, B., M. Zibulevsky, Blind Source Separation by Sparse Decomposition in a Signal Dictionary, in Neural Computation 13, pp. 863-882. 2001.
7. L. Benaroya, L. M. Donagh, F. Bimbot, and R. Gribonval, Non negative sparse representation for wiener based source separation with a single sensor, in Acoustics, Speech, and Signal Processing, IEEE International Conference on, 2003, pp. 613-616.
8. M. N. Schmidt and R. K. Olsson, Single-channel speech separation using sparse nonnegative matrix factorization, in International Conference on Spoken Language Processing (INTERSPEECH), 2006.
9. T. Virtanen, Sound source separation using sparse coding with temporal continuity objective, in International Computer Music Conference, ICMC, 2003.
10. Smaragdis, P. Raj, B. and Shashanka, M.V. 2007. Supervised and Semi-Supervised Separation of Sounds from Single-Channel Mixtures. In proceedings of ICA 2007. London, UK. September 2007.
11. Raj, B.; Smaragdis, P. 2005. Latent Variable Decomposition of Spectrograms for single channel speaker separation. In Proceedings of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, October, 2005.
12. Shashanka, M.V., B. Raj, P. Smaragdis, 2007. Sparse Overcomplete Latent Variable Decoposition of Counts Data. In Neural Information Processing Systems (NIPS), Vancouver, BC, Canada. December 2007.
13. Brand, M.E. Pattern Discovery via Entropy Minimization. In Uncertainty 99, AIS-TATS99, 1999.
14. Corless, R.M., G.H. Gonnet, D.E.G. Hare, D.J. Jeffrey, and D.E. Knuth. On the Lambert W Function. Advances in Computational Mathematics, 1996.
15. Bouguila N. and D. Ziou. Using unsupervised learning of a finite Dirichlet mixture model to improve pattern recognition applications, Pattern Recognition Letters, Volume 26, Issue 12, September 2005.
16. Hinneburg, A., Gabriel, H.-H. and Gohr, A. Bayesian Folding-In with Dirichlet Kernels for PLSI, in Seventh IEEE International Conference on Data Mining, Oct. 2007
17. Févotte, C., R. Gribonval and E. Vincent. 2005. BSS EVAL Toolbox User Guide, IRISA Technical Report 1706, Rennes, France, April 2005.