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arXiv · 2603.26135

TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

Amar Almaini·Jakob Folz·Ghadeer Ashour
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Abstract

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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Amar Almaini, Jakob Folz, Ghadeer Ashour. 2026-03-27. TinyML for Acoustic Anomaly Detection in IoT Sensor Networks. https://doi.org/10.1109/iceccme64568.2025.11277514

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