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Contribution of Statistical Tests to Sparseness-Based Blind Source Separation

Si Mohamed Aziz Sbai 1, 2 Abdeldjalil Aissa El Bey 3, 1 Dominique Pastor 4, 1
3 Lab-STICC_TB_CACS_COM
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
4 Lab-STICC_TB_CID_TOMS
Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance
Abstract : We address the problem of blind source separation in the underdetermined mixture case. Two statistical tests are proposed to reduce the number of empirical parameters involved in standard sparseness-based underdetermined blind source separation (UBSS) methods. The first test performs multisource selection of the suitable time-frequency points for source recovery and is full automatic. The second one is dedicated to autosource selection for mixing matrix estimation and requires fixing two parameters only, regardless of the instrumented SNRs. We experimentally show that the use of these tests incurs no performance loss and even improves the performance of standard weak-sparseness UBSS approaches.
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Submitted on : Monday, October 8, 2012 - 2:44:15 PM
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Si Mohamed Aziz Sbai, Abdeldjalil Aissa El Bey, Dominique Pastor. Contribution of Statistical Tests to Sparseness-Based Blind Source Separation. Eurasip Journal on Applied Signal Processing, Hindawi Publishing Corporation, 2012, 2012 (169), ⟨10.1186/1687-6180-2012-169⟩. ⟨hal-00739565⟩

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