Non-negative Dynamical System with Application to Speech and Audio

TR Image
Result of applying NDS.

Non-negative data arise in a variety of important signal processing domains, such as power spectra of signals, pixels in images, and count data. This paper introduces a novel non-negative dynamical system (NDS) for sequences of such data, and describes its application to modeling speech and audio power spectra. The NDS model can be interpreted both as an adaptation of linear dynamical systems (LDS) to non-negative data, and as an extension of non-negative matrix factorization (NMF) to support Markovian dynamics. Learning and inference algorithms were derived and experiments on speech enhancement were conducted by training sparse non-negative dynamical systems on speech data and adapting a noise model to the unknown noise condition. Results show that the model can capture the dynamics of speech in a useful way.


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    •  NEWS    ICASSP 2013: 9 publications by Jonathan Le Roux, Dehong Liu, Robert A. Cohen, Dong Tian, Shantanu D. Rane, Jianlin Guo, John R. Hershey, Shinji Watanabe, Petros T. Boufounos, Zafer Sahinoglu and Anthony Vetro
      Date: May 26, 2013
      Where: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
      MERL Contacts: Dehong Liu; Jianlin Guo; Anthony Vetro; Petros T. Boufounos; Jonathan Le Roux
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