TinyML for Acoustic Anomaly Detection in IoT Sensor Networks
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 th...
| Authors | Amar Almaini, Jakob Folz, Ghadeer Ashour |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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