Shift-invariant sparse coding for single channel blind source separation

Blumensath, T. and Davies, M. E. (2005) Shift-invariant sparse coding for single channel blind source separation. In, Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS'05), Rennes, FR, Centre National de la Reseacrhe Scientifique, 75-78.


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In this paper we present results on single channel blind source separation
based on a shift-invariant sparse coding model [1], [2] and
[3]. This model learns a set of time-domain features from a single
observation of the mixed signals. The found features can often
be associated with a single source and can therefore be used to
reconstruct the individual source signals. This is shown in this
paper on two real world examples, the separation of fetal and maternal
heartbeats from a single ECG recording and the separation
of singing and accompanying guitar from a musical recording. In
the first problem we learn two features, one representing the fetal
heartbeat and one representing the maternal heartbeat. In the second
example we learn a much larger set to model the more complex
source signals and therefore introduce a clustering method to
associate the different features with each of the sources

Item Type: Conference or Workshop Item (Paper)
Related URLs:
Subjects: Q Science > QA Mathematics
Divisions : University Structure - Pre August 2011 > School of Mathematics
Faculty of Engineering and the Environment > Institute of Sound and Vibration Research > Signal Processing & Control Research Group
ePrint ID: 151931
Accepted Date and Publication Date:
November 2005Published
Date Deposited: 01 Jul 2010 11:28
Last Modified: 31 Mar 2016 13:24

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