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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.20103 |
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| _version_ | 1866912602628554752 |
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| author | Ciapponi, Stefano Mannini, Leonardo Scanferla, Jarek Anderle, Matteo Farella, Elisabetta |
| author_facet | Ciapponi, Stefano Mannini, Leonardo Scanferla, Jarek Anderle, Matteo Farella, Elisabetta |
| contents | This paper introduces WrenNet, an efficient neural network enabling real-time multi-species bird audio classification on low-power microcontrollers for scalable biodiversity monitoring. We propose a semi-learnable spectral feature extractor that adapts to avian vocalizations, outperforming standard mel-scale and fully-learnable alternatives. On an expert-curated 70-species dataset, WrenNet achieves up to 90.8\% accuracy on acoustically distinctive species and 70.1\% on the full task. When deployed on an AudioMoth device ($\leq$1MB RAM), it consumes only 77mJ per inference. Moreover, the proposed model is over 16x more energy-efficient compared to Birdnet when running on a Raspberry Pi 3B+. This work demonstrates the first practical framework for continuous, multi-species acoustic monitoring on low-power edge devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20103 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers Ciapponi, Stefano Mannini, Leonardo Scanferla, Jarek Anderle, Matteo Farella, Elisabetta Sound Computational Engineering, Finance, and Science This paper introduces WrenNet, an efficient neural network enabling real-time multi-species bird audio classification on low-power microcontrollers for scalable biodiversity monitoring. We propose a semi-learnable spectral feature extractor that adapts to avian vocalizations, outperforming standard mel-scale and fully-learnable alternatives. On an expert-curated 70-species dataset, WrenNet achieves up to 90.8\% accuracy on acoustically distinctive species and 70.1\% on the full task. When deployed on an AudioMoth device ($\leq$1MB RAM), it consumes only 77mJ per inference. Moreover, the proposed model is over 16x more energy-efficient compared to Birdnet when running on a Raspberry Pi 3B+. This work demonstrates the first practical framework for continuous, multi-species acoustic monitoring on low-power edge devices. |
| title | Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers |
| topic | Sound Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.20103 |