发表机构
École Polytechnique; Freie Universität Berlin; Berlin University of Applied Sciences (BHT); HES-SO Valais-Wallis; Inria（巴黎综合理工学院; 柏林自由大学; 柏林应用科技大学; 瓦莱州瓦利斯高等专业学院; 法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究开发了PolyChirp方法，结合多技术实现低功耗传感器的多物种鸟鸣分类，性能优于现有技术，可同时分类10种鸟类，满足野外长期运行的资源要求。
AI 中文摘要
TinyML领域的最新进展表明，基于微控制器的低功耗硬件可在单节电池续航下，利用声学传感器数据实时完成整个繁殖期的鸟类物种监测。然而，迄今为止低功耗微控制器的现有技术仅能实现单一物种的二分类。相比之下，实际的动物群监测部署往往需要同时针对多个物种。为应对这一挑战，我们开发了PolyChirp，该方法结合了生物领域专业知识、自动化数据集整理、神经网络架构优化及新型硬件，以实现野外多类别的鸟类物种检测。PolyChirp基于全新设计的小型多类别模型，这些模型利用了最新的微控制器及带有神经网络处理单元（NPU）的硬件加速技术。我们评估了这些模型的预测性能，并在通用微控制器硬件上测量了它们的计算性能——包括内存占用、延迟和能耗。我们的结果表明，PolyChirp不仅在单一物种二分类任务上优于现有技术，还能同时实现多达10个物种的稳健分类，同时仍符合传感器的资源限制，该传感器需在单节电池续航下于野外运行整个季节。
英文摘要
Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real time based on acoustic sensor data for an entire breeding period on a single battery charge. However, the state of the art on low-power microcontrollers was so far limited to binary classification of a single species. In contrast, real fauna monitoring deployments often target multiple species simultaneously. To address this challenge we develop PolyChirp, an approach combining biological domain expertise, automated dataset curation, neural architecture optimization and novel hardware to achieve multiclass bird species detection in the wild. PolyChirp is based on newly designed tiny multiclass models that leverage recent microcontrollers and hardware acceleration with a neural processing unit (NPU). We evaluate the predictive performance of these models, and we measure their computational performance -- flash footprint, latency, energy consumption -- on common microcontroller hardware. Our results demonstrate that PolyChirp matches or exceeds the TinyChirp architectures retrained under our protocol on single-species detection, and further achieves robust classification of up to 10 species simultaneously (macro F2 up to 0.97), while still fitting the flash, latency and energy budget of a low-power microcontroller sensor. A data-driven front-end redesign additionally makes on-device mel feature extraction 7x to 11x cheaper.
Journal refIEEE International Symposium on the Internet of Sounds (IS2) 2026
