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Underdetermined Blind Audio Source Separation Using Modal Decomposition

Abstract

This paper introduces new algorithms for the blind separation of audio sources using modal decomposition. Indeed, audio signals and, in particular, musical signals can be well approximated by a sum of damped sinusoidal (modal) components. Based on this representation, we propose a two-step approach consisting of a signal analysis (extraction of the modal components) followed by a signal synthesis (grouping of the components belonging to the same source) using vector clustering. For the signal analysis, two existing algorithms are considered and compared: namely the EMD (empirical mode decomposition) algorithm and a parametric estimation algorithm using ESPRIT technique. A major advantage of the proposed method resides in its validity for both instantaneous and convolutive mixtures and its ability to separate more sources than sensors. Simulation results are given to compare and assess the performance of the proposed algorithms.

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Correspondence to Abdeldjalil Aïssa-El-Bey.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Aïssa-El-Bey, A., Abed-Meraim, K. & Grenier, Y. Underdetermined Blind Audio Source Separation Using Modal Decomposition. J AUDIO SPEECH MUSIC PROC. 2007, 085438 (2007). https://doi.org/10.1155/2007/85438

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Keywords

  • Parametric Estimation
  • Estimation Algorithm
  • Acoustics
  • Signal Analysis
  • Empirical Mode Decomposition