- 期刊
Automatic Recognition of Bird Songs Using Cepstral Coefficients
並列摘要
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.