Beyond the narrowband approximation: wideband convex methods for under-determined reverberant audio source separation

  • Authors:

  • Matthieu Kowalski;Emmanuel Vincent;Rémi Gribonval

  • Affiliations:

  • Laboratoire des Signaux et Systèmes, UMR, CNRS, SUPELEC-Univ Paris-Sud, Gif-sur-Yvette Cedex, France;INRIA, Centre Inria Rennes-Bretagne Atlantique, Rennes Cedex, France;INRIA, Centre Inria Rennes-Bretagne Atlantique, Rennes Cedex, France

  • Venue:
  • IEEE Transactions on Audio, Speech, and Language Processing - Special issue on processing reverberant speech: methodologies and applications
  • Year:
  • 2010

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Abstract

We consider the problem of extracting the source signals from an under-determined convolutive mixture assuming known mixing filters. State-of-the-art methods operate in the time-frequency domain and rely on narrowband approximation of the convolutive mixing process by complex-valued multiplication in each frequency bin. The source signals are then estimated by minimizing either a mixture fitting cost or a l1 source sparsity cost, under possible constraints on the number of active sources. In this paper, we define a wideband l2 mixture fitting cost circumventing the above approximation and investigate the use of a l1,2 mixed-norm cost promoting disjointness of the source time-frequency representations. We design a family of convex functionals combining these costs and derive suitable optimization algorithms. Experiments indicate that the proposed wideband methods result in a signal-to-distortion ratio improvement of 2 to 5 dB compared to the state-of-the-art on reverberant speech mixtures.