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Proceedings Paper

Independent component analysis for audio signal separation
Author(s): Jens Wellhausen; Volker Gnann
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Paper Abstract

In this paper an audio separation algorithm is presented, which is based on Independent Component Analysis (ICA). Audio separation could be the basis for many applications for example in the field of telecommunications, quality enhancement of audio recordings or audio classification tasks. Well known ICA algorithms are not usable for real-world recordings at the time, because they are designed for signal mixtures based on linear and over time constant mixing matrices. To adapt a standard ICA algorithm for real-world two-channel auditory scenes with two audio sources, the input audio streams are segmented in the time domain and a constant mixing matrix within a segment is assumed. The next steps are a time-delay estimation for each audio source in the mixture and a determination of the number of existing sources. In the following processing steps, for each source the input signals are time shifted and a standard ICA for linear mixtures is performed. After that, the remaining tasks are an evaluation of the ICA results and the construction of the resulting audio streams containing the separated sources.

Paper Details

Date Published: 24 October 2005
PDF: 8 pages
Proc. SPIE 6015, Multimedia Systems and Applications VIII, 60151I (24 October 2005); doi: 10.1117/12.631373
Show Author Affiliations
Jens Wellhausen, RWTH Aachen Univ. (Germany)
Volker Gnann, RWTH Aachen Univ. (Germany)

Published in SPIE Proceedings Vol. 6015:
Multimedia Systems and Applications VIII
Anthony Vetro; Chang Wen Chen; C.-C. J. Kuo; Tong Zhang; Qi Tian; John R. Smith, Editor(s)

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