Automatic Recognition of Bird Songs Using Cepstral Coefficients

10.6302/JITA.200605_1(1).0003

Chang-Hsing Lee；Yeuan-Kuen Lee；Ren-Zhuang Huang

birdsong recognition ； linear discriminant analysis ； LPCC ； MFCC

Journal of Information Technology and Applications(資訊科技與應用期刊)

1卷1期（2006 / 05 / 01）

17 - 23

英文

In this paper we propose a method to automatically identify birds from the sounds they generate. First, each syllable corresponding to a piece of vocalization is segmented. For each syllable, the averaged LPCCs (ALPCC) and averaged MFCCs (AMFCC) over all frames in a syllable are calculated as the vocalization features. Linear discriminant analysis (LDA) is exploited to increase the classification accuracy at a lower dimensional feature vector space. In our experiments, AMFCC usually outperforms ALPCC. If a codebook consisting of several representative feature vectors is used to model the syllables of the same bird species, the average classification accuracy is 87% for the recognition of 420 bird species.