Classification of Bryde's whale individuals using high-resolution time-frequency transforms and support vector...

Whales generate vocalizations which may, deliberately or not, encode caller identity cues. In this study, we analyze calls produced by Bryde's whales and recorded by ocean-bottom arrays of hydrophones deployed close to the Costa Rica Rift in the Panama Basin. These repetitive calls, consisting of two main frequency components at ∼20 and ∼36 Hz, have been shown to follow five coherent spatiotemporal tracks. Here, we use a high-resolution time-frequency transform, the fourth-order Fourier synchrosqueezing transform, to extract time-frequency characteristics (ridges) from each call to appraise their suitability for identifying individuals from each other. Focusing on high-quality calls recorded less than 5 km from their source, we then cluster these ridges using a support vector machine model resulting in an average cross-validation error of ∼11% and balanced accuracy of ∼86 ± 5%. Comparing these results with those obtained using the standard short-time Fourier transform, k-means clustering, and lower-quality signals, the Fourier synchrosqueezing transform approach, coupled with support vector machines, substantially improves classification. Consequently, the Bryde's whale calls potentially contain individual-specific information, suggesting that individuals can be studied using ocean-bottom data.