Harbour porpoises are well-suited for passive acoustic monitoring (PAM) as they produce highly stereotyped narrow-band high-frequency (NBHF) echolocation clicks. PAM systems must be coupled with a classification algorithm to identify the signals of interest. Here, the authors present a harbour porpoise click classifier (PorCC) developed in matlab, which uses the coefficients of two logistic regression models in a decision-making pathway to assign candidate signals to one of three categories: high-quality clicks (HQ), low-quality clicks (LQ), or high-frequency noise. The receiver operating characteristics of PorCC was compared to that of PAMGuard's Porpoise Click Detector/Classifier Module. PorCC outperformed PAMGuard's classifier achieving higher hit rates (correctly classified clicks) and lower false alarm levels (noise classified as HQ or LQ clicks). Additionally, the detectability index (d′) for HQ clicks for PAMGuard was 2.2 (overall d′ = 2.0) versus 4.1 for PorCC (overall d′ = 3.4). PorCC classification algorithm is a rapid and highly accurate method to classify NBHF clicks, which could be applied for real time monitoring, as well as to study harbour porpoises, and potentially other NBHF species, throughout their distribution range from data collected using towed hydrophones or static recorders. Moreover, PorCC is suitable for studies of acoustic communication of porpoises.
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