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A machine learning pipeline for classification of cetacean echolocation clicks in large underwater acoustic datasets
- Source :
- PLoS Computational Biology, Vol 17, Iss 12, p e1009613 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
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 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.<br />Author summary Machine learning algorithms have proven to be effective for tools for detection and classification tasks in many fields, however, these processes are generally data-hungry and their use in marine acoustics has been limited by a lack of large labeled datasets for algorithms to learn from. In underwater acoustic recordings, many signals generated by animals, human activities and physical processes mingle together, and their sounds can change depending on ocean temperatures, locations, and behavior. Manual classification of these datasets is unmanageable without an in-depth knowledge of the acoustic context of each recording location. This paper presents a process which combines unsupervised and supervised learning phases and expert oversight to generate and use large datasets for acoustic classification of marine mammal and human-generated signals. Unsupervised learning is used to automatically generate the large training datasets needed to teach a supervised learning algorithm to correctly classify seven different signal types commonly recorded in the Southern California Bight. Using this process, researchers with large unlabeled acoustic datasets can begin to take advantage of widespread advances in machine learning.
- Subjects :
- Databases, Factual
Computer science
Beaked Whales
Marine and Aquatic Sciences
Social Sciences
computer.software_genre
Signal
California
Machine Learning
Remote Sensing
Software Design
Broadband
Cluster Analysis
Psychology
Biology (General)
Mammals
Animal Behavior
Ecology
Physics
Eukaryota
Computational Theory and Mathematics
Data Interpretation, Statistical
Modeling and Simulation
Physical Sciences
Vertebrates
Engineering and Technology
Unsupervised learning
Algorithms
Network Analysis
Research Article
Computer and Information Sciences
QH301-705.5
Dolphins
Marine Biology
Sonar
Context (language use)
Human echolocation
Machine learning
Cellular and Molecular Neuroscience
Deep Learning
Acoustic Signals
Genetics
Animals
Marine Mammals
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Behavior
business.industry
Deep learning
Supervised learning
Whales
Organisms
Computational Biology
Biology and Life Sciences
Acoustics
Pipeline (software)
Signaling Networks
ComputingMethodologies_PATTERNRECOGNITION
Echolocation
Amniotes
Earth Sciences
Cetacea
Artificial intelligence
business
Bioacoustics
Zoology
computer
Unsupervised Machine Learning
Subjects
Details
- ISSN :
- 15537358
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....3fc0e981e0f1c834d8a7c53e8e8779bb