Fast and Versatile Blind Separation of Diverse Sounds Using Closed-Form Estimation of Probability Density Functions of Sources


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Title:	Fast and Versatile Blind Separation of Diverse Sounds Using Closed-Form Estimation of Probability Density Functions of Sources
Authors:	Hiroshi Saruwatari Yu Takahashi Kentaro Tachibana Yoshimitsu Mori Shigeki Miyabe Kiyohiro Shikano Akira Tanaka
Issue Date:	Dec-2009
Publisher:	IEEE
Start page:	249
End page:	252
Abstract:	In this paper, we propose a fast and versatile blind source separation including closed-form estimation of sources' probability density functions (PDFs), where the ICA's activation function is automatically adapted to various noise conditions. In the proposed method, closed-form second-order ICA and closed-form PDF estimation are introduced as a computational-cost-efficient preprocessing to extract sources' PDFs. Compared with various type of conventional ICAs, e.g., fixed activation-function type and ML-based type, our proposed algorithm can give a faster and higher convergence. Experimental assessment reveals that the proposed method is versatile for handling non-speech sound sources.
Description:	CAMSAP2009: the 3rd International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, December 13-16, 2009, Aruba, Dutch Antilles.
URI:	http://hdl.handle.net/10061/7929
ISBN:	9781424451807
Rights:	Copyright 2009 IEEE
Text Version:	Publisher
Publisher DOI:	10.1109/CAMSAP.2009.5413289
Appears in Collections:	情報科学研究科 / Graduate School of Information Science

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