Multispecies discrimination of New World Warblers (Family Parulidae) using Hidden Markov models (HMMS)

Abstract

Bioacoustics allows researchers to study animals through their vocalizations with non-invasive methods. The analysis of the recordings is a difficult task that is best handled by machine learning methods. Hidden Markov Models (HMMs), which are machine learning methods in human speech processing, were developed and implemented for the discrimination of 11 genera and 43 species of the New World Warblers (family Parulidae). Based on the CLO-43SD database, the fundamental goal of the experiments was to determine the classification accuracy on the specific genus and species of birds. Through Mel-Frequency Cepstral Coefficients (MFCCs) along with log energy and time derivative features extracted from the vocalizations, HMMs containing 2 states with single underlying Gaussian Mixture Models (GMMs) generated classification accuracies 91.55% across 11 genera of birds and 63.92% for 43 species of birds. From the results, the framework could be applied to analysis of other birds for both classification and detection of vocalizations.