A novel and fast blind source separation algorithm for convolutive environment

Faezeh Marzban Rad, Mohammad Ali Masnadi-Shirazi

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In this paper a novel and fast algorithm for the blind source separation in convolutive media is introduced. This method estimates multiple independent source signals, using only their set of received convolutive mixtures. The number of sources and the delays in the arrival of their echoes are unknown. The channel is estimated by calculating the channel matrix which is not achieved in some other CBSS methods. In this algorithm the independent component analysis (ICA) is used as the first step to separate the signals, lags and noise components. In the second stage, a purely second-order statistic approach estimates the source signals, which is a novel CBSS algorithm. This unique structure results in an efficient and accurate estimation. Another new feature of our approach is the implementation of a fast estimator. The channel variations are usually slow compared to the sampling rate. Therefore, the fast estimator separates the source signals using only the received signals at the sampling instant, based on the estimated channel. The channel matrix and the separated source signals are updated by repeating CBSS process at regular intervals. The permutation ambiguity, which is a common problem in many separation methods, is resolved in this algorithm. This new approach is simpler, faster, more accurate and needs less memory compared to some methods recently introduced by others.
Original languageEnglish
Title of host publicationIECON 2010-36th Annual Conference on IEEE Industrial Electronics Society
Number of pages5
Publication statusPublished - 2010
Externally publishedYes
EventAnnual Conference of the IEEE Industrial Electronics Society 2010 - Glendale, United States of America
Duration: 7 Nov 201010 Nov 2010
Conference number: 36th


ConferenceAnnual Conference of the IEEE Industrial Electronics Society 2010
Abbreviated titleIECON 2010
CountryUnited States of America

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