在物联网设备上实现低功耗声学异常检测。
TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
- 从声音信号提取梅尔频率倒谱系数，用轻量神经网络分类。
- 在UrbanSound8K数据集上达91%准确率，正常与异常类平衡F1为0.91。
- 适合资源受限的边缘设备，提升实时性与隐私保护。
TinyML使微控制器上的实时、低功耗数据处理成为可能，适用于物联网传感器网络。本文提出一种紧凑的TinyML流程，用于检测物联网系统中环境声音的异常。声学监测可提升安全性和情境感知，但云端处理存在延迟高、功耗大和隐私风险等问题。本方案通过从声音信号中提取梅尔频率倒谱系数，并训练一个面向边缘设备优化的轻量级神经网络分类器来解决上述问题。模型在UrbanSound8K数据集上训练并评估，测试准确率达91%，正常与异常声音类别间的平衡F1-score为0.91。结果证明了嵌入式声学异常检测在可扩展、响应迅速的物联网部署中的可行性与可靠性。
原文摘要 · Abstract (English)
Tiny Machine Learning enables real-time, energy-efficient data processing directly on microcontrollers, making it ideal for Internet of Things sensor networks. This paper presents a compact TinyML pipeline for detecting anomalies in environmental sound within IoT sensor networks. Acoustic monitoring in IoT systems can enhance safety and context awareness, yet cloud-based processing introduces challenges related to latency, power usage, and privacy. Our pipeline addresses these issues by extracting Mel Frequency Cepstral Coefficients from sound signals and training a lightweight neural network classifier optimized for deployment on edge devices. The model was trained and evaluated using the UrbanSound8K dataset, achieving a test accuracy of 91% and balanced F1-scores of 0.91 across both normal and anomalous sound classes. These results demonstrate the feasibility and reliability of embedded acoustic anomaly detection for scalable and responsive IoT deployments.
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