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Adaptive sparse source separation with application to speech signals

Azizi, E ; Sharif University of Technology | 2007

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  1. Type of Document: Article
  2. DOI: 10.1109/ICSPC.2007.4728400
  3. Publisher: 2007
  4. Abstract:
  5. In this paper, a sparse component analysis algorithm is presented for the case in which the number of sources is less than or equal to the number of sensors, but the channel (mixing matrix) is time-varying. The method is based on a smoothed l0 norm for the sparsity criteria, and takes advantage of the idea that sparsity of the sources is decreased when they are mixed. The method is able to separate synthetic and speech data, which require very weak sparsity restrictions. It can separate up to 50 mixed signals while being adaptive to channel variation and robust against noise. © 2007 IEEE
  6. Keywords:
  7. Apartment houses ; Separation ; Signal analysis ; Signal processing ; Adaptive source separation ; Analysis algorithms ; Channel variations ; Mixed signals ; Mixing matrixes ; Smoothed l0 norm ; Sparse component analysis ; Speech datum ; Speech signals ; Time-varying ; Blind source separation
  8. Source: 2007 IEEE International Conference on Signal Processing and Communications, ICSPC 2007, Dubai, 24 November 2007 through 27 November 2007 ; 2007 , Pages 640-643 ; 9781424412365 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/4728400