TR2016-008
Deep Unfolding for Multichannel Source Separation
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- "Deep Unfolding for Multichannel Source Separation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2016.7471649, March 2016, pp. 121-125.BibTeX TR2016-008 PDF
- @inproceedings{Wisdom2016mar,
- author = {Wisdom, Scott and Hershey, John R. and Le Roux, Jonathan and Watanabe, Shinji},
- title = {Deep Unfolding for Multichannel Source Separation},
- booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
- year = 2016,
- pages = {121--125},
- month = mar,
- doi = {10.1109/ICASSP.2016.7471649},
- url = {https://www.merl.com/publications/TR2016-008}
- }
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- "Deep Unfolding for Multichannel Source Separation", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), DOI: 10.1109/ICASSP.2016.7471649, March 2016, pp. 121-125.
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Abstract:
Deep unfolding has recently been proposed to derive novel deep network architectures from model-based approaches. In this paper, we consider its application to multichannel source separation. We unfold a multichannel Gaussian mixture model (MCGMM), resulting in a deep MCGMM computational network that directly processes complex-valued frequency-domain multichannel audio and has an architecture defined explicitly by a generative model, thus combining the advantages of deep networks and model-based approaches. We further extend the deep MCGMM by modeling the GMM states using an MRF, whose unfolded mean-field inference updates add dynamics across layers. Experiments on source separation for multichannel mixtures of two simultaneous speakers shows that the deep MCGMM leads to improved performance with respect to the original MCGMM model.
Related News & Events
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NEWS MERL researchers present 12 papers at ICASSP 2016 Date: March 20, 2016 - March 25, 2016
Where: Shanghai, China
MERL Contacts: Petros T. Boufounos; Chiori Hori; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip V. Orlik; Anthony Vetro
Research Areas: Computational Sensing, Digital Video, Speech & Audio, Communications, Signal ProcessingBrief- MERL researchers have presented 12 papers at the recent IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which was held in Shanghai, China from March 20-25, 2016. ICASSP is the flagship conference of the IEEE Signal Processing Society, and the world's largest and most comprehensive technical conference focused on the research advances and latest technological development in signal and information processing, with more than 1200 papers presented and over 2000 participants.