{"ID":2866584,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.20103","arxiv_id":"2509.20103","title":"Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers","abstract":"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.","short_abstract":"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-...","url_abs":"https://arxiv.org/abs/2509.20103","url_pdf":"https://arxiv.org/pdf/2509.20103v1","authors":"[\"Stefano Ciapponi\",\"Leonardo Mannini\",\"Jarek Scanferla\",\"Matteo Anderle\",\"Elisabetta Farella\"]","published":"2025-09-24T13:27:46Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"cs.CE\"]","methods":"[]","has_code":false}