CONVEX LIKELIHOOD ALIGNMENTS FOR BIOACOUSTIC CLASSIFICATION

In this work, we propose a bioacoustic classification framework based on Gaussian mixture models (GMM) and archetypal analysis (AA). The framework utilizes acoustic topic modelling to obtain an intermediate symbolic representation where the discrimination between target classes is more evident than in the input feature space. The proposed framework utilizes the GMM as an acoustic topic model and weighted likelihoods obtained from this GMM are utilized as the intermediate symbolic representation. Class-specific archetypal dictionaries are used to obtain the proposed feature representation, designated as convex likelihood alignments (CLAs), from this intermediate representation. Class-specific signatures are highly evident in these CLAs making them an ideal representation for various bioacoustic classification tasks. Through experiments on two available datasets, it is shown that the proposed CLAs yield comparable or better results than state-of-art approaches.