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- W102532062 abstract "Sound source separation in a real-world indoor environment is an ill-formed problem because sound source mixing is affected by the number of sounds, sound source activities, and reverberation. In addition, blind source separation (BSS) suffers from a permutation ambiguity in a frequency domain processing. Conventional methods have two problems: (1) impractical assumptions that the number of sound sources is given, and (2) permutation resolution as a post processing. This paper presents a non-parametric Bayesian BBS called permutation-free infinite sparse factor analysis (PF-ISFA) that solves the two problems simultaneously. Experimental results show that PF-ISFA outperforms conventional complex ISFA in all measures of BSS_EVAL criteria. In particular, PF-ISFA improves Signal-to-Interference Ratio by 14.45 dB and 5.46 dB under RT60=30 ms and RT60=460 ms conditions, respectively." @default.
- W102532062 created "2016-06-24" @default.
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- W102532062 date "2012-01-01" @default.
- W102532062 modified "2023-09-26" @default.
- W102532062 title "Infinite Sparse Factor Analysis for Blind Source Separation in Reverberant Environments" @default.
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- W102532062 doi "https://doi.org/10.1007/978-3-642-34166-3_70" @default.
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