[2603.26135] TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

Computer Science > Machine Learning arXiv:2603.26135 (cs) [Submitted on 27 Mar 2026] Title:TinyML for Acoustic Anomaly Detection in IoT Sensor Networks Authors:Amar Almaini, Jakob Folz, Ghadeer Ashour View a PDF of the paper titled TinyML for Acoustic Anomaly Detection in IoT Sensor Networks, by Amar Almaini and 2 other authors View PDF HTML (experimental) 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. Subjects: Machine Learning (cs.LG) Cite as: arXiv:2603.26135 [cs.LG]   (or arXiv:2603.26135v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2...