1
|
Oestreich WK, Oliver RY, Chapman MS, Go MC, McKenna MF. Listening to animal behavior to understand changing ecosystems. Trends Ecol Evol 2024:S0169-5347(24)00145-9. [PMID: 38972787 DOI: 10.1016/j.tree.2024.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 06/11/2024] [Accepted: 06/14/2024] [Indexed: 07/09/2024]
Abstract
Interpreting sound gives powerful insight into the health of ecosystems. Beyond detecting the presence of wildlife, bioacoustic signals can reveal their behavior. However, behavioral bioacoustic information is underused because identifying the function and context of animals' sounds remains challenging. A growing acoustic toolbox is allowing researchers to begin decoding bioacoustic signals by linking individual and population-level sensing. Yet, studies integrating acoustic tools for behavioral insight across levels of biological organization remain scarce. We aim to catalyze the emerging field of behavioral bioacoustics by synthesizing recent successes and rising analytical, logistical, and ethical challenges. Because behavior typically represents animals' first response to environmental change, we posit that behavioral bioacoustics will provide theoretical and applied insights into animals' adaptations to global change.
Collapse
Affiliation(s)
| | - Ruth Y Oliver
- Bren School of Environmental Science and Management, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Melissa S Chapman
- National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Madeline C Go
- Monterey Bay Aquarium Research Institute, Moss Landing, CA, USA
| | - Megan F McKenna
- Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USA
| |
Collapse
|
2
|
Conant PC, Li P, Liu X, Klinck H, Fleishman E, Gillespie D, Nosal EM, Roch MA. Silbido profundo: An open source package for the use of deep learning to detect odontocete whistles. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2022; 152:3800. [PMID: 36586843 DOI: 10.1121/10.0016631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
This work presents an open-source matlab software package for exploiting recent advances in extracting tonal signals from large acoustic data sets. A whistle extraction algorithm published by Li, Liu, Palmer, Fleishman, Gillespie, Nosal, Shiu, Klinck, Cholewiak, Helble, and Roch [(2020). Proceedings of the International Joint Conference on Neural Networks, July 19-24, Glasgow, Scotland, p. 10] is incorporated into silbido, an established software package for extraction of cetacean tonal calls. The precision and recall of the new system were over 96% and nearly 80%, respectively, when applied to a whistle extraction task on a challenging two-species subset of a conference-benchmark data set. A second data set was examined to assess whether the algorithm generalized to data that were collected across different recording devices and locations. These data included 487 h of weakly labeled, towed array data collected in the Pacific Ocean on two National Oceanographic and Atmospheric Administration (NOAA) cruises. Labels for these data consisted of regions of toothed whale presence for at least 15 species that were based on visual and acoustic observations and not limited to whistles. Although the lack of per whistle-level annotations prevented measurement of precision and recall, there was strong concurrence of automatic detections and the NOAA annotations, suggesting that the algorithm generalizes well to new data.
Collapse
Affiliation(s)
- Peter C Conant
- Department of Computer Science, San Diego State University, San Diego, California 92182, USA
| | - Pu Li
- Department of Computer Science, San Diego State University, San Diego, California 92182, USA
| | - Xiaobai Liu
- Department of Computer Science, San Diego State University, San Diego, California 92182, USA
| | - Holger Klinck
- K. Lisa Yang Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, New York, New York 14850, USA
| | - Erica Fleishman
- College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis, Oregon 97331, USA
| | - Douglas Gillespie
- Sea Mammal Research Unit, Scottish Oceans Institute, University of St. Andrews, St. Andrews, KY16 9AJ, United Kingdom
| | - Eva-Marie Nosal
- Department of Ocean and Resources Engineering, University of Hawai'i at Mānoa, Honolulu, Hawaii 96822, USA
| | - Marie A Roch
- Department of Computer Science, San Diego State University, San Diego, California 92182, USA
| |
Collapse
|
3
|
Vella K, Capel T, Gonzalez A, Truskinger A, Fuller S, Roe P. Key Issues for Realizing Open Ecoacoustic Monitoring in Australia. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2021.809576] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Many organizations are attempting to scale ecoacoustic monitoring for conservation but are hampered at the stages of data management and analysis. We reviewed current ecoacoustic hardware, software, and standards, and conducted workshops with 23 participants across 10 organizations in Australia to learn about their current practices, and to identify key trends and challenges in their use of ecoacoustics data. We found no existing metadata schemas that contain enough ecoacoustics terms for current practice, and no standard approaches to annotation. There was a strong need for free acoustics data storage, discoverable learning resources, and interoperability with other ecological modeling tools. In parallel, there were tensions regarding intellectual property management, and siloed approaches to studying species within organizations across different regions and between organizations doing similar work. This research contributes directly to the development of an open ecoacoustics platform to enable the sharing of data, analyses, and tools for environmental conservation.
Collapse
|
4
|
Roch MA, Lindeneau S, Aurora GS, Frasier KE, Hildebrand JA, Glotin H, Baumann-Pickering S. Using context to train time-domain echolocation click detectors. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 149:3301. [PMID: 34241092 DOI: 10.1121/10.0004992] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 04/26/2021] [Indexed: 06/13/2023]
Abstract
This work demonstrates the effectiveness of using humans in the loop processes for constructing large training sets for machine learning tasks. A corpus of over 57 000 toothed whale echolocation clicks was developed by using a permissive energy-based echolocation detector followed by a machine-assisted quality control process that exploits contextual cues. Subsets of these data were used to train feed forward neural networks that detected over 850 000 echolocation clicks that were validated using the same quality control process. It is shown that this network architecture performs well in a variety of contexts and is evaluated against a withheld data set that was collected nearly five years apart from the development data at a location over 600 km distant. The system was capable of finding echolocation bouts that were missed by human analysts, and the patterns of error in the classifier consist primarily of anthropogenic sources that were not included as counter-training examples. In the absence of such events, typical false positive rates are under ten events per hour even at low thresholds.
Collapse
Affiliation(s)
- Marie A Roch
- Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720, USA
| | - Scott Lindeneau
- Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720, USA
| | - Gurisht Singh Aurora
- Department of Computer Science, San Diego State University, 5500 Campanile Drive, San Diego, California 92182-7720, USA
| | - Kaitlin E Frasier
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive #0205, La Jolla, California 92093, USA
| | - John A Hildebrand
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive #0205, La Jolla, California 92093, USA
| | - Hervé Glotin
- Université de Toulon, BP 20132, 83957 La Garde Cedex, France
| | - Simone Baumann-Pickering
- Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive #0205, La Jolla, California 92093, USA
| |
Collapse
|
5
|
Rasmussen JH, Širović A. Automatic detection and classification of baleen whale social calls using convolutional neural networks. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2021; 149:3635. [PMID: 34241118 DOI: 10.1121/10.0005047] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/30/2021] [Indexed: 06/13/2023]
Abstract
Passive acoustic monitoring has proven to be an indispensable tool for many aspects of baleen whale research. Manual detection of whale calls on these large data sets demands extensive manual labor. Automated whale call detectors offer a more efficient approach and have been developed for many species and call types. However, calls with a large level of variability such as fin whale (Balaenoptera physalus) 40 Hz call and blue whale (B. musculus) D call have been challenging to detect automatically and hence no practical automated detector exists for these two call types. Using a modular approach consisting of faster region-based convolutional neural network followed by a convolutional neural network, we have created automated detectors for 40 Hz calls and D calls. Both detectors were tested on recordings with high- and low density of calls and, when selecting for detections with high classification scores, they were shown to have precision ranging from 54% to 57% with recall ranging from 72% to 78% for 40 Hz and precision ranging from 62% to 64% with recall ranging from 70 to 73% for D calls. As these two call types are produced by both sexes, using them in long-term studies would remove sex-bias in estimates of temporal presence and movement patterns.
Collapse
Affiliation(s)
- Jeppe Have Rasmussen
- Department of Marine Biology, Texas A&M University at Galveston, Galveston, Texas 77554, USA
| | - Ana Širović
- Department of Marine Biology, Texas A&M University at Galveston, Galveston, Texas 77554, USA
| |
Collapse
|
6
|
Miralles A, Bruy T, Wolcott K, Scherz MD, Begerow D, Beszteri B, Bonkowski M, Felden J, Gemeinholzer B, Glaw F, Glöckner FO, Hawlitschek O, Kostadinov I, Nattkemper TW, Printzen C, Renz J, Rybalka N, Stadler M, Weibulat T, Wilke T, Renner SS, Vences M. Repositories for Taxonomic Data: Where We Are and What is Missing. Syst Biol 2020; 69:1231-1253. [PMID: 32298457 PMCID: PMC7584136 DOI: 10.1093/sysbio/syaa026] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/20/2020] [Accepted: 03/24/2020] [Indexed: 12/05/2022] Open
Abstract
Natural history collections are leading successful large-scale projects of specimen digitization (images, metadata, DNA barcodes), thereby transforming taxonomy into a big data science. Yet, little effort has been directed towards safeguarding and subsequently mobilizing the considerable amount of original data generated during the process of naming 15,000-20,000 species every year. From the perspective of alpha-taxonomists, we provide a review of the properties and diversity of taxonomic data, assess their volume and use, and establish criteria for optimizing data repositories. We surveyed 4113 alpha-taxonomic studies in representative journals for 2002, 2010, and 2018, and found an increasing yet comparatively limited use of molecular data in species diagnosis and description. In 2018, of the 2661 papers published in specialized taxonomic journals, molecular data were widely used in mycology (94%), regularly in vertebrates (53%), but rarely in botany (15%) and entomology (10%). Images play an important role in taxonomic research on all taxa, with photographs used in >80% and drawings in 58% of the surveyed papers. The use of omics (high-throughput) approaches or 3D documentation is still rare. Improved archiving strategies for metabarcoding consensus reads, genome and transcriptome assemblies, and chemical and metabolomic data could help to mobilize the wealth of high-throughput data for alpha-taxonomy. Because long-term-ideally perpetual-data storage is of particular importance for taxonomy, energy footprint reduction via less storage-demanding formats is a priority if their information content suffices for the purpose of taxonomic studies. Whereas taxonomic assignments are quasifacts for most biological disciplines, they remain hypotheses pertaining to evolutionary relatedness of individuals for alpha-taxonomy. For this reason, an improved reuse of taxonomic data, including machine-learning-based species identification and delimitation pipelines, requires a cyberspecimen approach-linking data via unique specimen identifiers, and thereby making them findable, accessible, interoperable, and reusable for taxonomic research. This poses both qualitative challenges to adapt the existing infrastructure of data centers to a specimen-centered concept and quantitative challenges to host and connect an estimated $ \le $2 million images produced per year by alpha-taxonomic studies, plus many millions of images from digitization campaigns. Of the 30,000-40,000 taxonomists globally, many are thought to be nonprofessionals, and capturing the data for online storage and reuse therefore requires low-complexity submission workflows and cost-free repository use. Expert taxonomists are the main stakeholders able to identify and formalize the needs of the discipline; their expertise is needed to implement the envisioned virtual collections of cyberspecimens. [Big data; cyberspecimen; new species; omics; repositories; specimen identifier; taxonomy; taxonomic data.].
Collapse
Affiliation(s)
- Aurélien Miralles
- Departement Origins and Evolution, Institut Systématique, Evolution, Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, EPHE, 57 rue Cuvier, CP50, 75005 Paris, France
- Systematic Botany and Mycology, University of Munich (LMU), Menzingerstraße 67, 80638 Munich, Germany
| | - Teddy Bruy
- Departement Origins and Evolution, Institut Systématique, Evolution, Biodiversité (ISYEB), Muséum national d’Histoire naturelle, CNRS, Sorbonne Université, EPHE, 57 rue Cuvier, CP50, 75005 Paris, France
- Systematic Botany and Mycology, University of Munich (LMU), Menzingerstraße 67, 80638 Munich, Germany
| | - Katherine Wolcott
- Systematic Botany and Mycology, University of Munich (LMU), Menzingerstraße 67, 80638 Munich, Germany
- National Museum of Natural History, Smithsonian Institution, Washington, DC, USA
| | - Mark D Scherz
- Department of Herpetology, Zoologische Staatssammlung München (ZSM-SNSB), Münchhausenstraße 21, 81247 München, Germany
- Department of Biology, Universität Konstanz, Universitätstraße 10, 78464 Konstanz, Germany
| | - Dominik Begerow
- Department of Geobotany, Ruhr-University Bochum, Universitätsstraße 150, 44780 Bochum, Germany
| | - Bank Beszteri
- Department of Phycology, Faculty of Biology, University of Duisburg-Essen, Universitätsstraße 2, 45141 Essen, Germany
| | - Michael Bonkowski
- Department of Terrestrial Ecology, Center of Excellence in Plant Sciences (CEPLAS), Terrestrial Ecology, Institute of Zoology, University of Cologne, 50674 Köln, Germany
| | - Janine Felden
- MARUM - Center for Marine Environmental Sciences, University of Bremen, Leobenerstraße 8, 28359 Bremen, Germany
- Alfred Wegener Institute - Helmholtz Center for Polar- and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany
| | - Birgit Gemeinholzer
- Department of Systematic Botany, Justus Liebig University Gießen, Heinrich-Buff Ring 38, 35392 Giessen, Germany
| | - Frank Glaw
- Department of Herpetology, Zoologische Staatssammlung München (ZSM-SNSB), Münchhausenstraße 21, 81247 München, Germany
| | - Frank Oliver Glöckner
- Alfred Wegener Institute - Helmholtz Center for Polar- and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany
| | - Oliver Hawlitschek
- Department of Herpetology, Zoologische Staatssammlung München (ZSM-SNSB), Münchhausenstraße 21, 81247 München, Germany
- Department of Scientific Infrastructure, Centrum für Naturkunde (CeNak), Universität Hamburg, Martin-Luther-King-Platz 3, 20146 Hamburg, Germany
| | - Ivaylo Kostadinov
- GFBio - Gesellschaft für Biologische Daten e.V., c/o Research II, Campus Ring 1, 28759 Bremen, Germany
| | - Tim W Nattkemper
- Biodata Mining Group, Center of Biotechnology (CeBiTec), Bielefeld University, PO Box 100131, 33501 Bielefeld, Germany
| | - Christian Printzen
- Department of Botany and Molecular Evolution, Senckenberg Research Institute and Natural History Museum Frankfurt, Senckenberganlage 25, 60325 Frankfurt/Main, Germany
| | - Jasmin Renz
- Zooplankton Research Group, DZMB – Senckenberg am Meer, Martin-Luther-King Platz 3, 20146 Hamburg, Germany
| | - Nataliya Rybalka
- Department of Experimental Phycology and Culture Collection of Algae, University Göttingen, Nikolausberger-Weg 18, 37073 Göttingen, Germany
| | - Marc Stadler
- Department Microbial Drugs, Helmholtz Centre for Infection Research (HZI), and German Centre for Infection Research (DZIF), Partner Site Hannover-Braunschweig, Inhoffenstrasse 7, 38124 Braunschweig, Germany
| | - Tanja Weibulat
- GFBio - Gesellschaft für Biologische Daten e.V., c/o Research II, Campus Ring 1, 28759 Bremen, Germany
| | - Thomas Wilke
- Department of Animal Ecology and Systematics, Justus Liebig University Gießen, Heinrich-Buff Ring 26, 35392 Giessen, Germany
| | - Susanne S Renner
- Systematic Botany and Mycology, University of Munich (LMU), Menzingerstraße 67, 80638 Munich, Germany
| | - Miguel Vences
- Department of Evolutionary Biology, Zoological Institute, Technische Universität Braunschweig, Mendelssohnstraße 4, 38106 Braunschweig, Germany
| |
Collapse
|
7
|
Gentry KE, Lewis RN, Glanz H, Simões PI, Nyári ÁS, Reichert MS. Bioacoustics in cognitive research: Applications, considerations, and recommendations. WILEY INTERDISCIPLINARY REVIEWS. COGNITIVE SCIENCE 2020; 11:e1538. [PMID: 32548958 DOI: 10.1002/wcs.1538] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Revised: 05/15/2020] [Accepted: 05/18/2020] [Indexed: 12/23/2022]
Abstract
The multifaceted ability to produce, transmit, receive, and respond to acoustic signals is widespread in animals and forms the basis of the interdisciplinary science of bioacoustics. Bioacoustics research methods, including sound recording and playback experiments, are applicable in cognitive research that centers around the processing of information from the acoustic environment. We provide an overview of bioacoustics techniques in the context of cognitive studies and make the case for the importance of bioacoustics in the study of cognition by outlining some of the major cognitive processes in which acoustic signals are involved. We also describe key considerations associated with the recording of sound and its use in cognitive applications. Based on these considerations, we provide a set of recommendations for best practices in the recording and use of acoustic signals in cognitive studies. Our aim is to demonstrate that acoustic recordings and stimuli are valuable tools for cognitive researchers when used appropriately. In doing so, we hope to stimulate opportunities for innovative cognitive research that incorporates robust recording protocols. This article is categorized under: Neuroscience > Cognition Psychology > Theory and Methods Neuroscience > Behavior Neuroscience > Cognition.
Collapse
Affiliation(s)
- Katherine E Gentry
- Division of Habitat and Species Conservation, Florida Fish and Wildlife Conservation Commission, Tallahassee, Florida, USA
| | - Rebecca N Lewis
- Department of Earth and Environmental Sciences, The University of Manchester, Manchester, UK.,Chester Zoo, Chester, UK
| | - Hunter Glanz
- Statistics Department, California Polytechnic State University, San Luis Obispo, California, USA
| | - Pedro I Simões
- Departmento de Zoologia, Universidade Federal de Pernambuco, Recife, Brazil
| | - Árpád S Nyári
- Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, USA
| | - Michael S Reichert
- Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma, USA
| |
Collapse
|
8
|
Fregosi S, Harris DV, Matsumoto H, Mellinger DK, Negretti C, Moretti DJ, Martin SW, Matsuyama B, Dugan PJ, Klinck H. Comparison of fin whale 20 Hz call detections by deep-water mobile autonomous and stationary recorders. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2020; 147:961. [PMID: 32113295 DOI: 10.1121/10.0000617] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 01/06/2020] [Indexed: 06/10/2023]
Abstract
Acoustically equipped deep-water mobile autonomous platforms can be used to survey for marine mammals over intermediate spatiotemporal scales. Direct comparisons to fixed recorders are necessary to evaluate these tools as passive acoustic monitoring platforms. One glider and two drifting deep-water floats were simultaneously deployed within a deep-water cabled hydrophone array to quantitatively assess their survey capabilities. The glider was able to follow a pre-defined track while float movement was somewhat unpredictable. Fin whale (Balaenoptera physalus) 20 Hz pulses were recorded by all hydrophones throughout the two-week deployment. Calls were identified using a template detector, which performed similarly across recorder types. The glider data contained up to 78% fewer detections per hour due to increased low-frequency flow noise present during glider descents. The glider performed comparably to the floats and fixed recorders at coarser temporal scales; hourly and daily presence of detections did not vary by recorder type. Flow noise was related to glider speed through water and dive state. Glider speeds through water of 25 cm/s or less are suggested to minimize flow noise and the importance of glider ballasting, detector characterization, and normalization by effort when interpreting glider-collected data and applying it to marine mammal density estimation are discussed.
Collapse
Affiliation(s)
- Selene Fregosi
- Cooperative Institute for Marine Resources Studies, Oregon State University and National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory, 2030 Southeast Marine Science Drive, Newport, Oregon 97365, USA
| | - Danielle V Harris
- Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, Fife, United Kingdom
| | - Haruyoshi Matsumoto
- Cooperative Institute for Marine Resources Studies, Oregon State University and National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory, 2030 Southeast Marine Science Drive, Newport, Oregon 97365, USA
| | - David K Mellinger
- Cooperative Institute for Marine Resources Studies, Oregon State University and National Oceanic and Atmospheric Administration Pacific Marine Environmental Laboratory, 2030 Southeast Marine Science Drive, Newport, Oregon 97365, USA
| | - Christina Negretti
- Department of Animal and Rangeland Sciences, College of Agricultural Sciences, Oregon State University, Corvallis, Oregon 97331, USA
| | - David J Moretti
- Naval Undersea Warfare Center, Newport, Rhode Island 02841, USA
| | - Stephen W Martin
- National Marine Mammal Foundation, San Diego, California 92106, USA
| | - Brian Matsuyama
- National Marine Mammal Foundation, San Diego, California 92106, USA
| | - Peter J Dugan
- Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, New York 14850, USA
| | - Holger Klinck
- Center for Conservation Bioacoustics, Cornell Lab of Ornithology, Cornell University, Ithaca, New York 14850, USA
| |
Collapse
|
9
|
Bianco MJ, Gerstoft P, Traer J, Ozanich E, Roch MA, Gannot S, Deledalle CA. Machine learning in acoustics: Theory and applications. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019; 146:3590. [PMID: 31795641 DOI: 10.1121/1.5133944] [Citation(s) in RCA: 140] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 10/14/2019] [Indexed: 06/10/2023]
Abstract
Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.
Collapse
Affiliation(s)
- Michael J Bianco
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA
| | - Peter Gerstoft
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA
| | - James Traer
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Emma Ozanich
- Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093, USA
| | - Marie A Roch
- Department of Computer Science, San Diego State University, San Diego, California 92182, USA
| | - Sharon Gannot
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan 5290002, Israel
| | - Charles-Alban Deledalle
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California 92093, USA
| |
Collapse
|
10
|
Darras K, Batáry P, Furnas BJ, Grass I, Mulyani YA, Tscharntke T. Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2019; 29:e01954. [PMID: 31206926 DOI: 10.1002/eap.1954] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 06/09/2023]
Abstract
Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.
Collapse
Affiliation(s)
- Kevin Darras
- Agroecology, Department of Crop Sciences, University of Goettingen, Grisebachstrasse 6, 37077, Göttingen, Germany
| | - Péter Batáry
- Agroecology, Department of Crop Sciences, University of Goettingen, Grisebachstrasse 6, 37077, Göttingen, Germany
- Lendület Landscape and Conservation Ecology, Institute of Ecology and Botany, MTA Centre for Ecological Research, Alkotmány u. 2-4, 2163, Vácrátót, Hungary
| | - Brett J Furnas
- Wildlife Investigations Laboratory, California Department of Fish and Wildlife, 1701 Nimbus Road, Suite D, Sacramento, California, 95670, USA
| | - Ingo Grass
- Agroecology, Department of Crop Sciences, University of Goettingen, Grisebachstrasse 6, 37077, Göttingen, Germany
| | - Yeni A Mulyani
- Department of Forest Resources Conservation and Ecotourism, Faculty of Forestry, Bogor Agricultural University, Bogor, Indonesia
| | - Teja Tscharntke
- Agroecology, Department of Crop Sciences, University of Goettingen, Grisebachstrasse 6, 37077, Göttingen, Germany
| |
Collapse
|
11
|
Jones DOB, Gates AR, Huvenne VAI, Phillips AB, Bett BJ. Autonomous marine environmental monitoring: Application in decommissioned oil fields. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 668:835-853. [PMID: 30870752 DOI: 10.1016/j.scitotenv.2019.02.310] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 02/19/2019] [Accepted: 02/19/2019] [Indexed: 06/09/2023]
Abstract
Hundreds of Oil & Gas Industry structures in the marine environment are approaching decommissioning. In most areas decommissioning operations will need to be supported by environmental assessment and monitoring, potentially over the life of any structures left in place. This requirement will have a considerable cost for industry and the public. Here we review approaches for the assessment of the primary operating environments associated with decommissioning - namely structures, pipelines, cuttings piles, the general seabed environment and the water column - and show that already available marine autonomous systems (MAS) offer a wide range of solutions for this major monitoring challenge. Data of direct relevance to decommissioning can be collected using acoustic, visual, and oceanographic sensors deployed on MAS. We suggest that there is considerable potential for both cost savings and a substantial improvement in the temporal and spatial resolution of environmental monitoring. We summarise the trade-offs between MAS and current conventional approaches to marine environmental monitoring. MAS have the potential to successfully carry out much of the monitoring associated with decommissioning and to offer viable alternatives where a direct match for the conventional approach is not possible.
Collapse
Affiliation(s)
- Daniel O B Jones
- National Oceanography Centre, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK.
| | - Andrew R Gates
- National Oceanography Centre, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK
| | - Veerle A I Huvenne
- National Oceanography Centre, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK
| | - Alexander B Phillips
- National Oceanography Centre, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK
| | - Brian J Bett
- National Oceanography Centre, University of Southampton Waterfront Campus, Southampton SO14 3ZH, UK
| |
Collapse
|
12
|
Sugai LSM, Silva TSF, Ribeiro JW, Llusia D. Terrestrial Passive Acoustic Monitoring: Review and Perspectives. Bioscience 2018. [DOI: 10.1093/biosci/biy147] [Citation(s) in RCA: 149] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Larissa Sayuri Moreira Sugai
- Universidade Estadual Paulista, Instituto de Biociências, in São Paulo, Brazil
- Instituto de Geociências e Ciências Exatas at the Universidade Estadual Paulista, Ecosystem Dynamics Observatory, in São Paulo
- Terrestrial Ecology Group, in the Departamento de Ecología, Universidad Autónoma de Madrid, Spain
| | - Thiago Sanna Freire Silva
- Instituto de Geociências e Ciências Exatas at the Universidade Estadual Paulista, Ecosystem Dynamics Observatory, in São Paulo
| | - José Wagner Ribeiro
- Universidade Estadual Paulista, Instituto de Biociências, in São Paulo, Brazil
| | - Diego Llusia
- Laboratório de Herpetologia e Comportamento Animal, in the Departamento de Ecologia, at the Instituto de Ciências Biológicas, Universidade Federal de Goiás, in Goiás, Brazil
- Terrestrial Ecology Group, in the Departamento de Ecología, Universidad Autónoma de Madrid, Spain
| |
Collapse
|
13
|
Gibb R, Browning E, Glover‐Kapfer P, Jones KE. Emerging opportunities and challenges for passive acoustics in ecological assessment and monitoring. Methods Ecol Evol 2018. [DOI: 10.1111/2041-210x.13101] [Citation(s) in RCA: 184] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Rory Gibb
- Department of Genetics, Evolution and EnvironmentCentre for Biodiversity and Environment ResearchUniversity College London London UK
| | - Ella Browning
- Department of Genetics, Evolution and EnvironmentCentre for Biodiversity and Environment ResearchUniversity College London London UK
- Institute of ZoologyZoological Society of London London UK
| | - Paul Glover‐Kapfer
- WWF‐UKLiving Planet Centre Woking UK
- Flora & Fauna International David Attenborough Building Cambridge UK
| | - Kate E. Jones
- Department of Genetics, Evolution and EnvironmentCentre for Biodiversity and Environment ResearchUniversity College London London UK
| |
Collapse
|
14
|
Deichmann JL, Acevedo‐Charry O, Barclay L, Burivalova Z, Campos‐Cerqueira M, d'Horta F, Game ET, Gottesman BL, Hart PJ, Kalan AK, Linke S, Nascimento LD, Pijanowski B, Staaterman E, Mitchell Aide T. It's time to listen: there is much to be learned from the sounds of tropical ecosystems. Biotropica 2018. [DOI: 10.1111/btp.12593] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Jessica L. Deichmann
- Center for Conservation and Sustainability Smithsonian Conservation Biology Institute National Zoological Park Washington DC USA
| | - Orlando Acevedo‐Charry
- Sieve Analytics San Juan PR USA
- Colección de Sonidos Ambientales Instituto de Investigación de Recursos Biológicos Alexander von Humboldt Bogotá Colombia
| | - Leah Barclay
- Queensland Conservatorium Research Centre Griffith University Nathan Qld Australia
| | - Zuzana Burivalova
- Woodrow Wilson School of Public and International Affairs Princeton University Princeton NJ USA
| | | | - Fernando d'Horta
- Graduate Program in Genetics, Conservation and Evolutionary Biology INPA Manaus AM Brazil
| | - Edward T. Game
- Global Science The Nature Conservancy Brisbane Qld Australia
| | - Benjamin L. Gottesman
- Department of Forestry and Natural Resources Purdue University West Lafayette IN USA
| | - Patrick J. Hart
- Department of Biology University of Hawaii at Hilo Hilo HI USA
| | - Ammie K. Kalan
- Department of Primatology Max Planck Institute for Evolutionary Anthropology Leipzig Germany
| | - Simon Linke
- Australian Rivers Institute Griffith University Nathan Qld Australia
| | - Leandro Do Nascimento
- Department of Wildland Resources and Ecology Center Utah State University Logan UT USA
| | - Bryan Pijanowski
- Department of Forestry and Natural Resources Purdue University West Lafayette IN USA
| | - Erica Staaterman
- Bureau of Ocean Energy Management Office of Environmental Programs Sterling VA USA
- Beneath the Waves, Inc. Herndon VA USA
| | - T. Mitchell Aide
- Sieve Analytics San Juan PR USA
- Department of Biology University of Puerto Rico San Juan PR USA
| |
Collapse
|
15
|
Biodiversity Monitoring in Changing Tropical Forests: A Review of Approaches and New Opportunities. REMOTE SENSING 2017. [DOI: 10.3390/rs9101059] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|