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Solsona-Berga A, DeAngelis AI, Cholewiak DM, Trickey JS, Mueller-Brennan L, Frasier KE, Van Parijs SM, Baumann-Pickering S. Machine learning with taxonomic family delimitation aids in the classification of ephemeral beaked whale events in passive acoustic monitoring. PLoS One 2024; 19:e0304744. [PMID: 38833504 PMCID: PMC11149863 DOI: 10.1371/journal.pone.0304744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 05/16/2024] [Indexed: 06/06/2024] Open
Abstract
Passive acoustic monitoring is an essential tool for studying beaked whale populations. This approach can monitor elusive and pelagic species, but the volume of data it generates has overwhelmed researchers' ability to quantify species occurrence for effective conservation and management efforts. Automation of data processing is crucial, and machine learning algorithms can rapidly identify species using their sounds. Beaked whale acoustic events, often infrequent and ephemeral, can be missed when co-occurring with signals of more abundant, and acoustically active species that dominate acoustic recordings. Prior efforts on large-scale classification of beaked whale signals with deep neural networks (DNNs) have approached the class as one of many classes, including other odontocete species and anthropogenic signals. That approach tends to miss ephemeral events in favor of more common and dominant classes. Here, we describe a DNN method for improved classification of beaked whale species using an extensive dataset from the western North Atlantic. We demonstrate that by training a DNN to focus on the taxonomic family of beaked whales, ephemeral events were correctly and efficiently identified to species, even with few echolocation clicks. By retrieving ephemeral events, this method can support improved estimation of beaked whale occurrence in regions of high odontocete acoustic activity.
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Affiliation(s)
- Alba Solsona-Berga
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Annamaria I. DeAngelis
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, Massachusetts, United States of America
| | - Danielle M. Cholewiak
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, Massachusetts, United States of America
| | - Jennifer S. Trickey
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Liam Mueller-Brennan
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, Massachusetts, United States of America
| | - Kaitlin E. Frasier
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Sofie M. Van Parijs
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, Massachusetts, United States of America
| | - Simone Baumann-Pickering
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
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Kather V, Seipel F, Berges B, Davis G, Gibson C, Harvey M, Henry LA, Stevenson A, Risch D. Development of a machine learning detector for North Atlantic humpback whale song. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2024; 155:2050-2064. [PMID: 38477612 DOI: 10.1121/10.0025275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/22/2024] [Indexed: 03/14/2024]
Abstract
The study of humpback whale song using passive acoustic monitoring devices requires bioacousticians to manually review hours of audio recordings to annotate the signals. To vastly reduce the time of manual annotation through automation, a machine learning model was developed. Convolutional neural networks have made major advances in the previous decade, leading to a wide range of applications, including the detection of frequency modulated vocalizations by cetaceans. A large dataset of over 60 000 audio segments of 4 s length is collected from the North Atlantic and used to fine-tune an existing model for humpback whale song detection in the North Pacific (see Allen, Harvey, Harrell, Jansen, Merkens, Wall, Cattiau, and Oleson (2021). Front. Mar. Sci. 8, 607321). Furthermore, different data augmentation techniques (time-shift, noise augmentation, and masking) are used to artificially increase the variability within the training set. Retraining and augmentation yield F-score values of 0.88 on context window basis and 0.89 on hourly basis with false positive rates of 0.05 on context window basis and 0.01 on hourly basis. If necessary, usage and retraining of the existing model is made convenient by a framework (AcoDet, acoustic detector) built during this project. Combining the tools provided by this framework could save researchers hours of manual annotation time and, thus, accelerate their research.
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Affiliation(s)
- Vincent Kather
- Audio Communication and Technology, Technical University Berlin, Einsteinufer 17c, 10587, Berlin, Germany
| | - Fabian Seipel
- Audio Communication and Technology, Technical University Berlin, Einsteinufer 17c, 10587, Berlin, Germany
| | - Benoit Berges
- Wageningen Marine Research, Wageningen University and Research, IJmuiden, Noord Holland, 1976 CP, Netherlands
| | - Genevieve Davis
- National Oceanic and Atmospheric Administration (NOAA) Northeast Fisheries Science Center, 166 Water Street, Woods Hole, Massachusetts 02543, USA
| | - Catherine Gibson
- School of Biological Sciences, Queens University Belfast, Belfast, BT9 5DL, Northern Ireland
| | - Matt Harvey
- Google Inc., Mountain View, California 94043, USA
| | - Lea-Anne Henry
- School of GeoSciences, University of Edinburgh, James Hutton Road, EH9 3FE, Edinburgh, Scotland
| | | | - Denise Risch
- Scottish Association for Marine Science, University of Highlands and Islands, Oban, PA37 1QJ, Scotland
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Fleishman E, Cholewiak D, Gillespie D, Helble T, Klinck H, Nosal EM, Roch MA. Ecological inferences about marine mammals from passive acoustic data. Biol Rev Camb Philos Soc 2023; 98:1633-1647. [PMID: 37142263 DOI: 10.1111/brv.12969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/06/2023]
Abstract
Monitoring on the basis of sound recordings, or passive acoustic monitoring, can complement or serve as an alternative to real-time visual or aural monitoring of marine mammals and other animals by human observers. Passive acoustic data can support the estimation of common, individual-level ecological metrics, such as presence, detection-weighted occupancy, abundance and density, population viability and structure, and behaviour. Passive acoustic data also can support estimation of some community-level metrics, such as species richness and composition. The feasibility of estimation and certainty of estimates is highly context dependent, and understanding the factors that affect the reliability of measurements is useful for those considering whether to use passive acoustic data. Here, we review basic concepts and methods of passive acoustic sampling in marine systems that often are applicable to marine mammal research and conservation. Our ultimate aim is to facilitate collaboration among ecologists, bioacousticians, and data analysts. Ecological applications of passive acoustics require one to make decisions about sampling design, which in turn requires consideration of sound propagation, sampling of signals, and data storage. One also must make decisions about signal detection and classification and evaluation of the performance of algorithms for these tasks. Investment in the research and development of systems that automate detection and classification, including machine learning, are increasing. Passive acoustic monitoring is more reliable for detection of species presence than for estimation of other species-level metrics. Use of passive acoustic monitoring to distinguish among individual animals remains difficult. However, information about detection probability, vocalisation or cue rate, and relations between vocalisations and the number and behaviour of animals increases the feasibility of estimating abundance or density. Most sensor deployments are fixed in space or are sporadic, making temporal turnover in species composition more tractable to estimate than spatial turnover. Collaborations between acousticians and ecologists are most likely to be successful and rewarding when all partners critically examine and share a fundamental understanding of the target variables, sampling process, and analytical methods.
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Affiliation(s)
- Erica Fleishman
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, OR, 97331, USA
| | - Danielle Cholewiak
- Northeast Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Woods Hole, MA, 02543, USA
| | - Douglas Gillespie
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St Andrews, St Andrews, KY16 9XL, UK
| | - Tyler Helble
- Naval Information Warfare Center Pacific, San Diego, CA, 92152, USA
| | - Holger Klinck
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, NY, 14850, USA
| | - Eva-Marie Nosal
- Department of Ocean and Resources Engineering, University of Hawai'i at Manoa, Honolulu, HI, 96822, USA
| | - Marie A Roch
- Department of Computer Science, San Diego State University, San Diego, CA, 92182, USA
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Cohen RE, Frasier KE, Baumann-Pickering S, Wiggins SM, Rafter MA, Baggett LM, Hildebrand JA. Identification of western North Atlantic odontocete echolocation click types using machine learning and spatiotemporal correlates. PLoS One 2022; 17:e0264988. [PMID: 35324943 PMCID: PMC8946748 DOI: 10.1371/journal.pone.0264988] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 02/21/2022] [Indexed: 11/18/2022] Open
Abstract
A combination of machine learning and expert analyst review was used to detect odontocete echolocation clicks, identify dominant click types, and classify clicks in 32 years of acoustic data collected at 11 autonomous monitoring sites in the western North Atlantic between 2016 and 2019. Previously-described click types for eight known odontocete species or genera were identified in this data set: Blainville's beaked whales (Mesoplodon densirostris), Cuvier's beaked whales (Ziphius cavirostris), Gervais' beaked whales (Mesoplodon europaeus), Sowerby's beaked whales (Mesoplodon bidens), and True's beaked whales (Mesoplodon mirus), Kogia spp., Risso's dolphin (Grampus griseus), and sperm whales (Physeter macrocephalus). Six novel delphinid echolocation click types were identified and named according to their median peak frequencies. Consideration of the spatiotemporal distribution of these unidentified click types, and comparison to historical sighting data, enabled assignment of the probable species identity to three of the six types, and group identity to a fourth type. UD36, UD26, and UD28 were attributed to Risso's dolphin (G. griseus), short-finned pilot whale (G. macrorhynchus), and short-beaked common dolphin (D. delphis), respectively, based on similar regional distributions and seasonal presence patterns. UD19 was attributed to one or more species in the subfamily Globicephalinae based on spectral content and signal timing. UD47 and UD38 represent distinct types for which no clear spatiotemporal match was apparent. This approach leveraged the power of big acoustic and big visual data to add to the catalog of known species-specific acoustic signals and yield new inferences about odontocete spatiotemporal distribution patterns. The tools and call types described here can be used for efficient analysis of other existing and future passive acoustic data sets from this region.
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Affiliation(s)
- Rebecca E. Cohen
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
- * E-mail:
| | - Kaitlin E. Frasier
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Simone Baumann-Pickering
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Sean M. Wiggins
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Macey A. Rafter
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - Lauren M. Baggett
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
| | - John A. Hildebrand
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California, United States of America
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