# TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

arXiv: https://arxiv.org/abs/2603.26135 · HTML: https://mlsanity.com/paper/2603.26135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 27 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [IoT Anomaly Detection Methods and Applications: A Survey](https://mlsanity.com/paper/2207.09092/) (2022)
- [Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2406.07250/) (2024)
- [Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms](https://mlsanity.com/paper/2001.10876/) (2020)
- [Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis](https://mlsanity.com/paper/2411.17733/) (2024)

## Abstract (arXiv)

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.