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  • 1
    Publication Date: 2023-02-16
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Sayigh, L., Janik, V., Jensen, F., Scott, M., Tyack, P., & Wells, R. The Sarasota Dolphin whistle database: a unique long-term resource for understanding dolphin communication. Frontiers in Marine Science, 9, (2022): 923046, https://doi.org/10.3389/fmars.2022.923046.
    Description: Common bottlenose dolphins (Tursiops truncatus) produce individually distinctive signature whistles that are learned early in life and that help animals recognize and maintain contact with conspecifics. Signature whistles are the predominant whistle type produced when animals are isolated from conspecifics. Health assessments of dolphins in Sarasota, Florida (USA) provide a unique opportunity to record signature whistles, as dolphins are briefly separated from conspecifics. Recordings were first made in the mid 1970’s, and then nearly annually since 1984. The Sarasota Dolphin Whistle Database (SDWD) now contains 926 recording sessions of 293 individual dolphins, most of known age, sex, and matrilineal relatedness. The longest time span over which an individual has been recorded is 43 years, and 85 individuals have been recorded over a decade or more. Here we describe insights about signature whistle structure revealed by this unique and expansive dataset. Signature whistles of different dolphins show great variety in their fundamental frequency contours. Signature whistle types (with ‘whistle type’ defined as all whistles visually categorized as sharing a particular frequency modulation pattern) can consist of a single stereotyped element, or loop (single-loop whistles), or of multiple stereotyped loops with or without gaps (multi-loop whistles). Multi-loop signature whistle types can also show extensive variation in both number and contour of loops. In addition, fundamental frequency contours of all signature whistle types can be truncated (deletions) or embellished (additions), and other features are also occasionally incorporated. However, even with these variable features, signature whistle types tend to be highly stereotyped and easily distinguishable due to the extensive variability in contours among individuals. In an effort to quantify this individual distinctiveness, and to compare it to other species, we calculated Beecher’s Information Statistic and found it to be higher than for any other animal signal studied so far. Thus, signature whistles have an unusually high capacity to convey information on individual identity. We briefly review the large range of research projects that the SDWD has enabled thus far, and look ahead to its potential to answer a broad suite of questions about dolphin communication.
    Description: Funding for data collection and analysis over the years has been provided by the National Science Foundation, The Royal Society of London, Dolphin Quest, Adelaide M. and Charles B. Link Foundation, Marine Mammal Commission, National Oceanic and Atmospheric Administration, Earthwatch Institute, Protect Wild Dolphins Fund of the Harbor Branch Oceanographic Institute, Grossman Family Foundation, WHOI Ocean Life Institute, Vulcan Machine Learning Center for Impact, and the Allen Institute for Artificial Intelligence. Current support for PT’s involvement is provided by the Office of Naval Research Grants N00014-18-1-2062 and N00014-20-1-2709 through a subaward from Carnegie Mellon University. Current support for LS’s involvement is provided by the Adelaide M. & Charles B. Link Foundation and Dolphin Quest.
    Keywords: Signature whistle ; Communication ; Cognition ; Database ; Individual identity
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 2
    Publication Date: 2023-03-08
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ferguson, S., Jensen, F., Hyer, M., Noble, A., Apprill, A., & Mooney, T. Ground-truthing daily and lunar patterns of coral reef fish call rates on a US Virgin Island reef. Aquatic Biology, 31, (2022): 77–87, https://doi.org/10.3354/ab00755.
    Description: Coral reefs comprise some of the most biodiverse habitats on the planet. These ecosystems face a range of stressors, making quantifying community assemblages and potential changes vital to effective management. To understand short- and long-term changes in biodiversity and detect early warning signals of decline, new methods for quantifying biodiversity at scale are necessary. Acoustic monitoring techniques have proven useful in observing species activities and biodiversity on coral reefs through aggregate approaches (i.e. energy as a proxy). However, few studies have ground-truthed these acoustic analyses with human-based observations. In this study, we sought to expand these passive acoustic methods by investigating biological sounds and fish call rates on a healthy reef, providing a unique set of human-confirmed, labeled acoustic observations. We analyzed acoustic data from Tektite Reef, St. John, US Virgin Islands, over a 2 mo period. A subset of acoustic files was manually inspected to identify recurring biotic sounds and quantify reef activity throughout the day. We found a high variety of acoustic signals in this soundscape. General patterns of call rates across time conformed to expectations, with dusk and dawn showing important and significantly elevated peaks in soniferous fish activity. The data reflected high variability in call rates across days and lunar phases. Call rates did not correspond to sound pressure levels, suggesting that certain call types may drive crepuscular trends in sound levels while lower-level critical calls, likely key for estimating biodiversity and behavior, may be missed by gross sound level analyses.
    Description: This research was funded by the National Science Foundation Biological Oceanography award 1536782. The experiments were conducted under National Park Service Scientific Research and Collecting Permits VIIS-2016-SCI-0017-20, and we thank the Park staff for their support.
    Keywords: Marine protected area ; Soundscape ; Noise ; Biodiversity ; Acoustic behavior ; Monitoring ; Tropics
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 3
    Publication Date: 2024-04-18
    Description: Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behavior. In the past twenty years, the volume of digitised wildlife sound available has massively increased, and automatic classification through deep learning now shows strong results. However, bioacoustics is not a single task but a vast range of small-scale tasks (such as individual ID, call type, emotional indication) with wide variety in data characteristics, and most bioacoustic tasks do not come with strongly-labelled training data. The standard paradigm of supervised learning, focussed on a single large-scale dataset and/or a generic pretrained algorithm, is insufficient. In this work we recast bioacoustic sound event detection within the AI framework of few-shot learning. We adapt this framework to sound event detection, such that a system can be given the annotated start/end times of as few as 5 events, and can then detect events in long-duration audio—even when the sound category was not known at the time of algorithm training. We introduce a collection of open datasets designed to strongly test a system’s ability to perform few-shot sound event detections, and we present the results of a public contest to address the task. Our analysis shows that prototypical networks are a very common used strategy and they perform well when enhanced with adaptations for general characteristics of animal sounds. However, systems with high time resolution capabilities perform the best in this challenge. We demonstrate that widely-varying sound event durations are an important factor in performance, as well as nonstationarity, i.e. gradual changes in conditions throughout the duration of a recording. For fine-grained bioacoustic recognition tasks without massive annotated training data, our analysis demonstrate that few-shot sound event detection is a powerful new method, strongly outperforming traditional signal-processing detection methods in the fully automated scenario.
    Keywords: Bioacoustics ; Deep learning ; Event detection ; Few-shot learning
    Repository Name: National Museum of Natural History, Netherlands
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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