Automatic recognition of the characteristic melody of bird vocalizations

We present a method for the automated recognition of birdsong syllables type. For that purpose, we automatically segment the birdsong to acoustic evens, and subsequently each segment is modeled by a GMM-based interpolation of the short-term energy of the dominant frequency component. Next, the parameters of the GMM model are fed to a classifier in order to recognize the birdsong syllables type. We evaluated the practical worth of this approach using publicly available field recordings of species Myrmotherula multostriata which were recorded in natural habitats. The experimental protocol was based on seventy-eight acoustic events, which were obtained after the automatic segmentation of the audio signal. We report recognition accuracy of up to 98%, depending on the classification method and the particular syllable type. Summarizing the experimental results obtained in this study, we concluded that the proposed method has good potential for achieving higher recognition accuracy, however additional work is needed for satisfying the needs of practical applications.