HMM-based modelling of individual syllables for bird species recognition from audio field recordings

This paper presents an automatic system for recognition of bird species from audio field recordings. The acoustic signal is first segmented into isolated time-frequency segments, each corresponding to an individual detected sinusoidal component. Each segment is represented by a temporal sequence of the frequency values of the detected sinusoid, referred to as frequency track. Hidden Markov models (HMMs) are employed to model the temporal evolution of frequency track features. Individual syllables of bird vocalisations are discovered using an unsupervised method based on dynamic time warping and agglomerative hierarchical clustering. The outcome of this is then employed to create individual HMMs for syllables of each species. Experiments are performed on over 33 hours of field recordings, containing 30 bird species. Evaluations demonstrate that the use of individual syllable HMMs provides over 40% error rate reduction over the use of single HMM for each bird species of the same complexity. The syllable HMM-based system recognises bird species with accuracy over 95% using 3 seconds of detected signal.