A Novel Denoising Method for Acoustic Target Classification in Wild Environment

摘要：

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.

关键词： wireless sensor surveillance network denoising KICA Renyi quadratic entropy estimator

作者: Yang Xu Zhang Xue-yuan Xie Dong-feng Li Bao-qing

作者单位: Shanghai Institute of Micro-system and Information Technology Shanghai 200050, China

会议类型: 国际会议

会议名称: 2011 International Conference on Computer Science and Network Technology(2011计算机科学与网络技术国际会议 ICCSNT 2011)

会议地点: 哈尔滨

会议语种:英文

页码: 1398-1402

在线出版日期: 2011-12-24（万方平台首次上网日期，不代表论文的发表时间）