Alternative Objective Functions for Deep Clustering

The recently proposed deep clustering framework represents a significant step towards solving the cocktail party problem. This study proposes and compares a variety of alternative objective functions for training deep clustering networks. In addition, whereas the original deep clustering work relied on k-means clustering for test-time inference, here we investigate inference methods that are matched to the training objective. Furthermore, we explore the use of an improved chimera network architecture for speech separation, which combines deep clustering with mask-inference networks in a multiobjective training scheme. The deep clustering loss acts as a regularizer while training the end-to-end mask inference network for best separation. With further iterative phase reconstruction, our best proposed method achieves a state-of-the-art 11.5 dB signal-to-distortion ratio (SDR) result on the publicly available wsj0-2mix dataset, with a much simpler architecture than the previous best approach.


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    •  NEWS   MERL presenting 9 papers at ICASSP 2018
      Date: April 15, 2018 - April 20, 2018
      Where: Calgary, AB
      MERL Contacts: Petros Boufounos; Takaaki Hori; Toshiaki Koike-Akino; Jonathan Le Roux; Dehong Liu; Hassan Mansour; Philip Orlik; Pu (Perry) Wang
      Research Areas: Computational Sensing, Digital Video, Speech & Audio
      • MERL researchers are presenting 9 papers at the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), which is being held in Calgary from April 15-20, 2018. Topics to be presented include recent advances in speech recognition, audio processing, and computational sensing. MERL is also a sponsor of the conference.

        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. The event attracts more than 2000 participants each year.