NDLI: A novel denoising method for acoustic target classification in wild environment

Content Provider	IEEE Xplore Digital Library
Author	Yang Xu Zhang Xue-yuan Xie Dong-feng Li Bao-qing
Copyright Year	2011
Description	Author affiliation: Wireless sensor network lab, Shanghai Institute of Micro-system and Information Technology, 200050, China (Yang Xu; Zhang Xue-yuan; Xie Dong-feng; Li Bao-qing)
Abstract	The acoustic recognition technology in wireless sensor surveillance network in wild environment is facing the challenge of the complicated and strong acoustic noise, especially the wind noise. Kernel Independent Component Analysis (KICA) is a non-linear method for blind source separation (BSS) technology which was wildly used in signal preprocessing. Considering the high computational complexity of KICA, an improved KICA algorithm is proposed based on the Renyi quadratic entropy estimator. A series of simulation experiment show that the improved KICA algorithm can well maintain the separating performance while reduce the computational complexity of KICA and the algorithm could be well utilized in denoising for the target classification system.
Starting Page	1398
Ending Page	1402
File Size	1110563
Page Count	5
File Format	PDF
ISBN	9781457715860
e-ISBN	9781457715877
DOI	10.1109/ICCSNT.2011.6182226
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bellows Renyi quadratic entropy estimator Acoustics Wireless sensor surveillance network Denoising KICA
Content Type	Text
Resource Type	Article

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