Blind Separation of Infinitely Many Sparse Sources
Konferenz: IWAENC 2012 - International Workshop on Acoustic Signal Enhancement
04.09.2012-06.09.2012 in Aachen, Germany
Tagungsband: IWAENC 2012
Seiten: 4Sprache: EnglischTyp: PDF
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Autoren:
Kameoka, Hirokazu (Graduate School of Information Science and Technology, The University of Tokyo )
Sato, Misa (School of Engineering, The University of Tokyo, Japan)
Ono, Takuma; Sagayama, Shigeki (Graduate School of Information Science and Technology, The University of Tokyo, Japan)
Ono, Nobutaka (Principles of Informatics Research Revision, National Institute of Informatics)
Inhalt:
This paper deals with the problem of underdetermined blind source separation (BSS) where the number of sources is unknown. We propose a BSS approach that simultaneously estimates the number of sources, separates the sources based on the sparseness of speech, and performs permutation alignment. We confirmed experimentally that reasonably good separation was obtained with the present method without specifying the number of sources. Index Terms — Underdetermined blind source separation, sparseness, Dirichlet process, variational inference