Vocalization data and scripts to model reindeer rut activity using on-animal acoustic recorders and machine learning ...
For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviou...
| Main Authors: | , , , |
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| Format: | Software |
| Language: | unknown |
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Zenodo
2024
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| Online Access: | https://dx.doi.org/10.5281/zenodo.11095409 https://zenodo.org/doi/10.5281/zenodo.11095409 |
| Summary: | For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study at the Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting reindeer were ... : Funding provided by: Natural Sciences and Engineering Research CouncilCrossref Funder Registry ID: https://ror.org/01h531d29Award Number: 327505 Funding provided by: NordForskCrossref Funder Registry ID: https://ror.org/05bqzfg94Award Number: 76915 ... |
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