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

• Main Author: Frasier, K.E (Author)
• Format: Article
• Language: English
• Published: Public Library of Science 2021
• Subjects: acoustics; Acoustics; algorithm; Algorithms; animal; Animals; article; biology; California; Cetacea; cluster analysis; Cluster Analysis; Computational Biology; Data Interpretation, Statistical; Databases, Factual; deep learning; Deep Learning; echolocation; Echolocation; factual database; machine learning; Machine Learning; nonhuman; physiology; pipeline; recall; software design; Software Design; statistical analysis; toothed whale; unsupervised machine learning; Unsupervised Machine Learning; Whales
• Online Access: View Fulltext in Publisher

 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 supervised learning phases with opportunities for expert oversight to label signals of interest. The method is illustrated with a case study using unsupervised clustering to identify five toothed whale echolocation click types and two anthropogenic signal categories. These categories are used to train a deep network to classify detected signals in either averaged time bins or as individual detections, in two independent datasets. Bin-level classification achieved higher overall precision (>99%) than click-level classification. However, click-level classification had the advantage of providing a label for every signal, and achieved higher overall recall, with overall precision from 92 to 94%. The results suggest that unsupervised learning is a viable solution for efficiently generating the large, representative training sets needed for applications of deep learning in passive acoustics. Copyright: © 2021 Kaitlin E. Frasier. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.