OpenSoundscape: An open‐source bioacoustics analysis package for Python
Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserve...
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| Veröffentlicht in: | Methods in ecology and evolution Jg. 14; H. 9; S. 2321 - 2328 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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London
John Wiley & Sons, Inc
01.09.2023
Wiley |
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| ISSN: | 2041-210X, 2041-210X |
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| Abstract | Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets.
We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data.
OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape.
By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation. |
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| AbstractList | Abstract Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation. Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation. Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated detection provides an effective means of monitoring sound‐producing species' occupancy and phenology and can lend insight into unobserved behaviours and patterns. The availability of low‐cost recording hardware has lowered barriers to large‐scale data collection, but technological barriers in data analysis remain a bottleneck for extracting biological insight from bioacoustic datasets. We provide a robust and open‐source Python toolkit for detecting and localizing biological sounds in acoustic data. OpenSoundscape provides access to automated acoustic detection, classification and localization methods through a simple and easy‐to‐use set of tools. Extensive documentation and tutorials provide step‐by‐step instructions and examples of end‐to‐end analysis of bioacoustic data. Here, we describe the functionality of this package and provide concise examples of bioacoustic analyses with OpenSoundscape. By providing an interface for bioacoustic data and methods, we hope this package will lead to increased adoption of bioacoustics methods and ultimately to enhanced insights for ecology and conservation. |
| Author | Lapp, Sam Syunkova, Alexandra Khilnani, Jatin Kitzes, Justin Rhinehart, Tessa Freeland‐Haynes, Louis |
| Author_xml | – sequence: 1 givenname: Sam orcidid: 0000-0003-1637-6822 surname: Lapp fullname: Lapp, Sam email: sam.lapp@pitt.edu organization: University of Pittsburgh – sequence: 2 givenname: Tessa orcidid: 0000-0002-4352-3464 surname: Rhinehart fullname: Rhinehart, Tessa organization: University of Pittsburgh – sequence: 3 givenname: Louis surname: Freeland‐Haynes fullname: Freeland‐Haynes, Louis organization: University of Pittsburgh – sequence: 4 givenname: Jatin surname: Khilnani fullname: Khilnani, Jatin organization: University of Pittsburgh – sequence: 5 givenname: Alexandra surname: Syunkova fullname: Syunkova, Alexandra organization: University of Pittsburgh – sequence: 6 givenname: Justin surname: Kitzes fullname: Kitzes, Justin organization: University of Pittsburgh |
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| Cites_doi | 10.1038/s41598‐019‐48909‐4 10.1098/rsif.2021.0297 10.21105/joss.05338 10.1002/wsb.1246 10.1121/1.4808713 10.1016/j.ecoinf.2021.101333 10.1002/wsb.1395 10.1038/s41598‐022‐26429‐y 10.1145/2833157.2833162 10.1111/2041‐210X.13711 10.1145/1873951.1874254 10.1002/rse2.125 10.1002/ecy.3329 10.51492/cfwj.107.5 10.1111/cobi.13718 10.1016/j.ecolind.2022.109128 10.1109/MCSE.2011.37 10.21105/joss.01237 10.1080/09524622.2016.1138415 10.1111/2041‐210X.13213 10.1007/s11263‐019‐01228‐7 10.1093/ornithapp/duac003 10.25080/Majora-7b98e3ed-003 10.1121/10.0007998 10.7717/peerj.13152 10.1109/MCSE.2007.55 10.1111/2041‐210X.13964 10.1002/rse2.294 10.25080/Majora-92bf1922-00a 10.1111/2041‐210X.14003 10.1080/09524622.2018.1503971 |
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| Copyright | 2023 The Authors. published by John Wiley & Sons Ltd on behalf of British Ecological Society. 2023. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2023. This work is published under Creative Commons Attribution License~https://creativecommons.org/licenses/by/3.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Snippet | Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and automated... Abstract Landscape‐scale bioacoustic projects have become a popular approach to biodiversity monitoring. Combining passive acoustic monitoring recordings and... |
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| SubjectTerms | acoustic monitoring Acoustic tracking Acoustics Algorithms automated detection Automation Bioacoustics Biodiversity Birds Classification Cost analysis Data analysis Data collection Deep learning Flexibility Localization Machine learning Metadata Monitoring OpenSoundscape Python Signal processing Software Sound detecting and ranging |
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| Title | OpenSoundscape: An open‐source bioacoustics analysis package for Python |
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