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Title: Nonparametric Bayesina Sparse Factor Analysis for Frequency Doain Blind Source Separation without Pearmuation Ambiguity
Authors: Nagira, Kohei
Otsuka, Takuma
Okuno, Hiroshi G.
Author's alias: 奥乃, 博
Issue Date: 22-Jan-2013
Publisher: SpringerOpen
Journal title: EURASIP Journal on Audio, Speech, and Music Processing
Volume: 2013
Thesis number: 4
Abstract: Blind source separation (BSS) and sound activity detection (SAD) from a sound source mixture with minimum prior information are two major requirements for computational auditory scene analysis that recognizes auditory events in many environments. In daily environments, BSS suffers from many problems such as reverberation, a permutation problem in frequency-domain processing, and uncertainty about the number of sources in the observed mixture. While many conventional BSS methods resort to a cascaded combination of subprocesses, e.g., frequency-wise separation and permutation resolution, to overcome these problems, their outcomes may be affected by the worst subprocess. Our aim is to develop a unified framework to cope with these problems. Our method, called permutation-free infinite sparse factor analysis (PF-ISFA), is based on a nonparametric Bayesian framework that enables inference without a pre-determined number of sources. It solves BSS, SAD and the permutation problem at the same time. Our method has two key ideas: unified source activities for all the frequency bins and the activation probabilities of all the frequency bins of all the sources. Experiments were carried out to evaluate the separation performance and the SAD performance under four reverberant conditions. For separation performance in the BSS_EVAL criteria, our method outperformed conventional complex ISFA under all conditions. For SAD performance, our method outperformed the conventional method by 5.9–0.5% in F-measure under the condition RT20 = 30–600 [ms], respectively.
Rights: © 2013 Nagira et al.; licensee Springer.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOI(Published Version): 10.1186/1687-4722-2013-4
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