Separating Underdetermined Convolutive Speech Mixtures

Michael Syskind Pedersen, DeLiang Wang, Jan Larsen, Ulrik Kjems

AbstractA limitation in many source separation tasks is that the number of source signals has to be known in advance. Further, in order to achieve good performance, the number of sources cannot exceed the number of sensors. In many real-world applications these limitations are too restrictive. We propose a method for underdetermined blind source separation of convolutive mixtures. The proposed framework is applicable for separation of instantaneous as well as convolutive speech mixtures. It is possible to iteratively extract each speech signal from the mixture by combining blind source separation techniques with binary time-frequency masking. In the proposed method, the number of source signals is not assumed to be known in advance and the number of sources is not limited to the number of microphones. Our approach needs only two microphones and the separated sounds are maintained as stereo signals.
Keywordsunderdetermined ICA, speech, time-frequency masking
TypeConference paper [With referee]
ConferenceICA 2006
EditorsJustinian Rosca, Deniz Erdogmus, José C. Príncipe, Simon Haykin
Year2006    Vol. 3889    pp. 674-681
PublisherSpringer Berlin / Heidelberg
SeriesLecture Notes in Computer Science
ISBN / ISSNISBN 3-540-32630-8
Electronic version(s)[pdf]
Publication linkhttp://www.springerlink.com/(5mek2yi4tvyqp455f5qmu245)/app/home/contribution.asp?referrer=parent&backto=issue,84,120;journal,10,3313;linkingpublicationresults,1:105633,1
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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