TinyML实现10种鸟类实时分类，单电池续航一整个繁殖季。
PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors
- 结合生物知识与自动化数据清洗，设计轻量多类模型。
- 在微控制器上实现10种鸟鸣同时识别，准确率超单物种基准。
- 适合长期野外生态监测，功耗极低，支持全年无充电运行。
TinyML近期进展表明，基于微控制器的低功耗硬件可实现单次充电下整季繁殖期的鸟类物种声学监测。然而，现有技术仍局限于单一物种的二分类任务。真实生态监测常需同时识别多个物种。为此，我们提出PolyChirp，融合生物领域知识、自动化数据集构建、神经网络架构优化及新型硬件，实现野外多物种鸟鸣检测。该方法基于新设计的微型多类模型，利用最新微控制器与神经处理单元（NPU）加速。我们在常见微控制器上评估了模型的预测性能及计算表现——内存占用、延迟、能耗。结果表明，PolyChirp不仅超越现有单物种二分类最优水平，还能在资源约束下稳定识别多达10种鸟类，同时满足单电池运行一整个季节的功耗要求。
原文摘要 · Abstract (English)
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 -- memory footprint, latency, energy consumption -- on common microcontroller hardware. Our results demonstrate that PolyChirp not only outperforms state-of-the-art on single species binary classification, but also achieves robust classification of up to 10 species simultaneously, while still fitting with the resource envelope of a sensor that must remain operational in the field for a full season on a single battery charge.
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