Automatic recognition of animal vocalizations using averaged MFCC and linear discriminant analysis

Authors:
Chang-Hsing Lee;Chih-Hsun Chou;Chin-Chuan Han;Ren-Zhuang Huang
Affiliations:
Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu 300, Taiwan, ROC;Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu 300, Taiwan, ROC;Department of Computer Science and Information Engineering, National United University, Miao-Li 360, Taiwan, ROC;Department of Computer Science and Information Engineering, Chung Hua University, Hsinchu 300, Taiwan, ROC
Venue:
Pattern Recognition Letters
Year:
2006

Citing 4
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Abstract

In this paper we propose a method that uses the averaged Mel-frequency cepstral coefficients (MFCCs) and linear discriminant analysis (LDA) to automatically identify animals from their sounds. First, each syllable corresponding to a piece of vocalization is segmented. The averaged MFCCs over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA), which finds out a transformation matrix that minimizes the within-class distance and maximizes the between-class distance, is utilized to increase the classification accuracy while to reduce the dimensionality of the feature vectors. In our experiment, the average classification accuracy is 96.8% and 98.1% for 30 kinds of frog calls and 19 kinds of cricket calls, respectively.