Bird species recognition from field recordings using HMM-based modelling of frequency tracks

Abstract

This paper presents an automatic system for recognition of bird species from field audio recordings. The proposed system employs a novel method for detection of sinusoidal components in the acoustic scene. This provides a segmentation of the signal and also feature representation of each segment in terms of frequencies over time, referred to as frequency track. We employ hidden Markov models (HMMs) to model the temporal evolution of frequency tracks. We demonstrate the effect of including local temporal dynamics of frequency tracks and HMM modelling parameters. Experiments are performed on over 33 hours of field recordings, containing 30 bird species. Evaluations demonstrate that the HMM-based temporal modelling provides considerable performance improvement over a system based on Gaussian mixture modelling. The proposed HMM-based system is capable of recognising bird species with accuracy over 85% from only 3 seconds of detected signal.