Soundbay: Deep Learning Framework for Marine Mammals and Bioacoustic Research
This paper presents Soundbay, an open-source Python framework that allows bio-acoustics and machine learning researchers to implement and utilize deep learning-based algorithms for acoustic audio analysis. Soundbay provides an easy and intuitive platform for applying existing models on one's da...
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| creator | Bressler, Noam Faran, Michael Galor, Amit Michelashvili, Michael Moshe Nachshon, Tomer Weiss, Noa |
| description | This paper presents Soundbay, an open-source Python framework that allows
bio-acoustics and machine learning researchers to implement and utilize deep
learning-based algorithms for acoustic audio analysis. Soundbay provides an
easy and intuitive platform for applying existing models on one's data or
creating new models effortlessly. One of the main advantages of the framework
is the capability to compare baselines on different benchmarks, a crucial part
of emerging research and development related to the usage of deep-learning
algorithms for animal call analysis. We demonstrate this by providing a
benchmark for cetacean call detection on multiple datasets. The framework is
publicly accessible via https://github.com/deep-voice/soundbay |
| doi_str_mv | 10.48550/arxiv.2311.04343 |
| format | Article |
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bio-acoustics and machine learning researchers to implement and utilize deep
learning-based algorithms for acoustic audio analysis. Soundbay provides an
easy and intuitive platform for applying existing models on one's data or
creating new models effortlessly. One of the main advantages of the framework
is the capability to compare baselines on different benchmarks, a crucial part
of emerging research and development related to the usage of deep-learning
algorithms for animal call analysis. We demonstrate this by providing a
benchmark for cetacean call detection on multiple datasets. The framework is
publicly accessible via https://github.com/deep-voice/soundbay</description><identifier>DOI: 10.48550/arxiv.2311.04343</identifier><language>eng</language><subject>Computer Science - Sound</subject><creationdate>2023-11</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2311.04343$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2311.04343$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Bressler, Noam</creatorcontrib><creatorcontrib>Faran, Michael</creatorcontrib><creatorcontrib>Galor, Amit</creatorcontrib><creatorcontrib>Michelashvili, Michael Moshe</creatorcontrib><creatorcontrib>Nachshon, Tomer</creatorcontrib><creatorcontrib>Weiss, Noa</creatorcontrib><title>Soundbay: Deep Learning Framework for Marine Mammals and Bioacoustic Research</title><description>This paper presents Soundbay, an open-source Python framework that allows
bio-acoustics and machine learning researchers to implement and utilize deep
learning-based algorithms for acoustic audio analysis. Soundbay provides an
easy and intuitive platform for applying existing models on one's data or
creating new models effortlessly. One of the main advantages of the framework
is the capability to compare baselines on different benchmarks, a crucial part
of emerging research and development related to the usage of deep-learning
algorithms for animal call analysis. We demonstrate this by providing a
benchmark for cetacean call detection on multiple datasets. The framework is
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bio-acoustics and machine learning researchers to implement and utilize deep
learning-based algorithms for acoustic audio analysis. Soundbay provides an
easy and intuitive platform for applying existing models on one's data or
creating new models effortlessly. One of the main advantages of the framework
is the capability to compare baselines on different benchmarks, a crucial part
of emerging research and development related to the usage of deep-learning
algorithms for animal call analysis. We demonstrate this by providing a
benchmark for cetacean call detection on multiple datasets. The framework is
publicly accessible via https://github.com/deep-voice/soundbay</abstract><doi>10.48550/arxiv.2311.04343</doi><oa>free_for_read</oa></addata></record> |
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| identifier | DOI: 10.48550/arxiv.2311.04343 |
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| subjects | Computer Science - Sound |
| title | Soundbay: Deep Learning Framework for Marine Mammals and Bioacoustic Research |
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