TinyML for Acoustic Anomaly Detection in IoT Sensor Networks

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.

Figures (6)

• Figure 1: TinyML pipeline for MFCC-based sound anomaly detection on IoT sensor nodes.
• Figure 2: Confusion matrix showing predicted vs. actual labels for the binary classification of urban sounds.
• Figure 3: ROC curve showing the model’s ability to separate normal and anomalous audio events.
• Figure 4: Precision-Recall curve highlighting the trade-off between detection sensitivity and alert specificity.
• Figure 5: Training and validation accuracy across 35 epochs. Early stopping triggered after performance plateaued.