# Enabling Multi-Species Bird Classification on Low-Power Bioacoustic Loggers

arXiv: https://arxiv.org/abs/2509.20103 · HTML: https://mlsanity.com/paper/2509.20103/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2025.

## Built on by (most cited first, 2 of 2)

- [Future of Edge AI in biodiversity monitoring](https://mlsanity.com/paper/2602.13496/) (2026)
- [PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors](https://mlsanity.com/paper/2608.23101/) (2026)

## Abstract (arXiv)

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 (≤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.