Graph-Based Clustering of Dolphin Whistles

An effective method for detecting the presence of dolphins is by using passive acoustic monitoring (PAM), where pod size indications can be estimated by counting individual whistles. The detection of dolphin whistles is commonly applied on a time-frequency representation, followed by denoising and whistle tracking to evaluate the number of whistles. However, due to harmonics, multipath and time-varying signal-to-noise ratio, a single dolphin whistle may be associated with multiple whistle-traces. Thus, as a first step towards evaluating dolphins' abundance, our goal is to cluster individual whistle traces into unique whistles. Our scheme measures the similarity between each pair of whistle traces, and estimates the likelihood of whistle traces sharing the same cluster. Clustering is formalized as an optimization problem, aims to maximize the stability of clusters. Formalizing the problem as a minimal-cut optimization on a graph provides an effective solution based on spectral decomposition of the graph-Laplacian. Our model of the likelihood sharing cluster provides a physically-meaningful method to calculate the graph's connectivity parameters, thereby leading to a robust blind clustering. Based on numerical simulations and real recordings of dolphin whistles at sea, we demonstrate the applicability of our solution and its advance beyond alternative approaches.