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Lookup NU author(s): Bin Gao,
Dr Wai Lok Woo,
Professor Satnam Dlay
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Blind source separation is an advanced statistical tool that has found widespread use in many signal processing applications. In particular Single Channel Blind Source Separation (SCBSS) has recently attracted a great deal of attention in Blind source separation fields. SCBSS attempts to discover from observed signals a set of hidden underlying sources that are as statistically independent as possible from only a single stream of observation data. This paper proposes a novel technique for separating single channel recording of audio mixture using a hybrid of maximum likelihood (ML) and maximum a posteriori (MAP) estimators. In addition, the new algorithm proposes a new approach that accounts for the time structure of the source signals by encoding them into a set of basis filters that are characteristically the most significant Real time testing of the new algorithm has been conducted and the obtained results are very encouraging.
Author(s): Gao B, Woo WL, Dlay SS
Editor(s): Leitgeb, E., Kogler, W., Ghassemlooy, Z.
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 6th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP 2008)
Year of Conference: 2008
Publisher: Graz University of Technology
Library holdings: Search Newcastle University Library for this item