Multispecies discrimination of whales (cetaceans) using Hidden Markov Models (HMMS)

Abstract

Hidden Markov Models (HMMs) were developed and implemented for the discrimination of 11 available Whales (Cetaceans). The primarily aims of the experiments were to explore the impact frame size and step size, feature vector size, and number of states for feature extraction and acoustic models on classification accuracy. Through the experiments using Mel-Frequency Cepstral Coefficients (MFCCs) extracted from the vocalizations (7 ms frame size and 6 ms step size), HMMs containing 4 states with single underlying Gaussian Mixture Model (GMM) yielded high classification accuracies ranging from 82.72% (9 classes) to 100.00% (1–3 classes), including discrimination of 84.11% (11 classes). From the results, the framework could be applied to the analysis of other marine mammals for the automatic classification and detection of vocalizations and species.