Multi-Channel Residual Learning for Marine Mammal Vocalization Classification

Abstract

Marine mammal vocalizations provide a reliable method of identifying most species. These vocalizations may be effectively captured by the passive acoustic monitoring of underwater acoustic signals, but this approach generates prohibitively large amounts of data, making a machine learning approach desirable. In this study, we demonstrate that residual learning networks may be highly effective at classifying vocalizations by species and investigate the utility of multi-channel spectrograms in training these networks. We find that classification performance can be improved by effectively preprocessing the acoustic data. A series of tests are used to assess optimal parameters for noise-removal, spectrographic window functions, preprocessing augmentations, and multi-channel spectrogram generation. While we find that our networks do learn additional information from multi-channel spectrograms, we demonstrate that single-channel spectrograms still offer superior classification performance in 32-class classification tasks.