Employing Stochastic Constrained Lms Algorithm for Asr Frontend Processing

نویسندگان

  • Michael Stadtschnitzer
  • Daniel Stein
  • Rolf Bardeli
چکیده

In scenarios with multiple input single output systems, the stochastic constrained least mean-squares (LMS) algorithm has been proven to be an effective approach. However, when only two input channels are available, it is unclear whether this approach still yields improvements. In this paper, we investigate the stableness and the robustness of the constrained LMS algorithm on “Track 1” of “2 CHiME Challenge” [1] and show that it leads to small yet consistent improvements on all signal-to-noise settings.

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تاریخ انتشار 2013