Sequential Blind Source Extraction For Quasi-Periodic Signals With Time-Varying Period

A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation mat...

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Bibliographic Details
Published in:	IEEE transactions on biomedical engineering Vol. 56; no. 3; pp. 646 - 655
Main Authors:	Tsalaile $^\ast$, Thato, Sameni, Reza, Sanei, Saeid, Jutten, Christian, Chambers, Jonathon
Format:	Journal Article
Language:	English
Published:	United States IEEE 01-03-2009 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Autocorrelation Blind source extraction (BSE) Computer Science Computer Simulation Data mining Delay estimation Electrocardiography Engineering Sciences Heart Heart Sounds Higher order statistics Humans Lungs Models, Statistical Normal Distribution quasi-periodic Respiratory Sounds second-order statistics Signal and Image processing Signal processing Signal processing algorithms Signal Processing, Computer-Assisted Source separation statistical nonstationarity Testing time-varying period quasi-periodic time-varying period Blind source extraction (BSE) statistical nonstationarity second-order statistics
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Summary:	A novel second-order-statistics-based sequential blind extraction algorithm for blind extraction of quasi-periodic signals, with time-varying period, is introduced in this paper. Source extraction is performed by sequentially converging to a solution that effectively diagonalizes autocorrelation matrices at lags corresponding to the time-varying period, which thereby explicitly exploits a key statistical nonstationary characteristic of the desired source. The algorithm is shown to have fast convergence and yields significant improvement in signal-to-interference ratio as compared to when the algorithm assumes a fixed period. The algorithm is further evaluated on the problem of separation of a heart sound signal from real-world lung sound recordings. Separation results confirm the utility of the introduced approach, and listening tests are employed to further corroborate the results.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	0018-9294 1558-2531
DOI:	10.1109/TBME.2008.2002141