An end-to-end approach for true detection of low frequency marine mammal vocalizations

Research into automated systems for detecting marine mammal vocalizations within acoustic recordings is expanding internationally due to the necessity to analyze large collections of data collected for passive acoustic monitoring. Recent work towards the development of such systems using Convolutional Neural Networks (CNNs) shows great promise and these systems are capable of generalizing to additional species without having to re-train the entire network [1]. However, to the best of our knowledge, the current deep learning implementations do not perform what we refer to as true detection. Instead these systems are simply capable of determining the presence or absence of a vocalization within a spectrogram. In this work we present a CNN trained on spectrograms containing labelled bounding boxes around low-frequency vocalizations produced by several species of marine mammals. In this way, the CNN can precisely detect vocalizations in terms of both time and frequency, while maintaining the advantage of being generalizable to additional species. [1] M. Thomas, B. Martin, K. Kowarski, B. Gaudet, and S. Stan, "Marine mammal species classification using convolutional neural networks and a novel acoustic representation," in ECML PKDD 2019 (Springer, Cham, 2019).