Bird Species Recognition by Comparing the HMMs of the Syllables

In this study, a bird species recognition system based on their sounds is proposed. In this system, the birdsong of a bird species is segmented into many syllables, from which several primary frequency sequences can be obtained. By using the statistics of the principle frequency sequences, all the syllables are clustered with the fuzzy C-mean clustering method so that each syllable group can be modeled by a hidden Markov model (HMM) characterizing the features of the song of the bird species. Using the Viterbi algorithm, the recognition process is achieved by finding the template bird species that has the most probable HMMs matching the frequency sequences of the test birdsong. Experimental results show that the proposed system can achieve a recognition rate of over 78% for 420 kinds of bird species.