Field Mixed Acoustic identification hybrid Systems Based on ICA and Improved GCA

Author

Li, Yaobo ; Ren, Zhiliang ; Chen, Gong ; Hu, Shengliang

Author_Institution

Dept. of Weaponry Eng., Naval Univ. of Eng., Wuhan

fYear

2006

fDate

20-23 Aug. 2006

Firstpage

117

Lastpage

121

Abstract

With independent component analysis (ICA) to realize the blind separation from mixed acoustic objects, an identification method based on improved gray correlation analysis (IGCA) is proposed through extracting linear prediction coefficient (LPC) feature. It is revealed that LPC is consistently better than wavelet energy feature, ICA is efficient algorithm to estimate the unknown signal level and IGCA which gets over the shortcomings of GCA model may reflect the difference and similarity of the influences of factors or characteristics effectively. The validity of the new systems is verified via examples in mixed acoustic objects identification system

Keywords

acoustic signal processing; feature extraction; identification; independent component analysis; prediction theory; wavelet transforms; field mixed acoustic object identification; hybrid system; improved gray correlation analysis; independent component analysis; linear prediction coefficient; wavelet energy feature; Acoustic noise; Acoustic waves; Biological system modeling; Data mining; Degradation; Feature extraction; Higher order statistics; Independent component analysis; Linear predictive coding; Predictive models; Feature; GCA; ICA; Identification; LPC;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Acquisition, 2006 IEEE International Conference on

Conference_Location

Weihai

Print_ISBN

1-4244-0528-9

Electronic_ISBN

1-4244-0529-7

Type

conf

DOI

10.1109/ICIA.2006.305924

Filename

4097857