A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets.

ABSTRACT: Machine learning algorithms, including recent advances in deep learning, are promising for tools for detection and classification of broadband high frequency signals in passive acoustic recordings. However, these methods are generally data-hungry and progress has been limited by challenges related to the lack of labeled datasets adequate for training and testing. Large quantities of known and as yet unidentified broadband signal types mingle in marine recordings, with variability introduced by acoustic propagation, source depths and orientations, and interacting signals. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. A signal classification pipeline is presented which combines unsupervised and superv

SUBMITTER: Frasier KE 

PROVIDER: S-EPMC8673644 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature