Classification of underwater mammals using feature extraction based on time-frequency analysis and BCM theory

被引：45

作者：

Huynh, QQ ^{[1
]}

Cooper, LN

Intrator, N

Shouval, H

机构：

[1] Brown Univ, Dept Phys, Providence, RI 02912 USA

[2] Brown Univ, Inst Brain & Neural Syst, Providence, RI 02912 USA

来源：

IEEE TRANSACTIONS ON SIGNAL PROCESSING | 1998年 / 46卷 / 05期

关键词：

classification; nonlinear feature extraction; time-frequency analysis; wavelets;

D O I：

10.1109/78.668783

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Underwater mammal sound classification is demonstrated using a novel application of wavelet time-frequency decomposition and feature extraction using a Bienenstock, Cooper, and Munro (BCM) unsupervised network. Different feature extraction methods and different wavelet representations are studied. The system achieves outstanding classification performance even when tested with mammal sounds recorded at very different locations (from those used for training). The improved results suggest that nonlinear feature extraction from wavelet representations outperforms different linear choices of basis functions.

引用

页码：1202 / 1207

页数：6

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