An acoustic sensing system for noise monitoring and source identification using transfer learning
Issued Date
2026-03-01
Resource Type
ISSN
09574174
Scopus ID
2-s2.0-105021123837
Journal Title
Expert Systems with Applications
Volume
299
Rights Holder(s)
SCOPUS
Bibliographic Citation
Expert Systems with Applications Vol.299 (2026)
Suggested Citation
Gunatilaka D., Sawangphol W., Charoenritthitham T., Kanjanapoo T., Burasotikul T., Pongprasit K. An acoustic sensing system for noise monitoring and source identification using transfer learning. Expert Systems with Applications Vol.299 (2026). doi:10.1016/j.eswa.2025.130014 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114551
Title
An acoustic sensing system for noise monitoring and source identification using transfer learning
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Increasing noise pollution in urban areas underscores the need for an autonomous system to monitor and control noise. Beyond detecting noise levels, identifying noise sources further improves noise management. This work presents a scalable IoT-based sensing platform for smart environment applications. The system integrates low-cost devices for acoustic measurement, edge devices to enable noise source identification, a back-end infrastructure crucial for efficient acoustic data and device management, and a web-based application facilitating noise data visualization. Our study explores three feature extraction techniques and eight Convolutional Neural Network (CNN)-based pre-trained models for noise classification on the resource-constrained Raspberry Pi platform and compares their performance. Leveraging pre-trained models helps speed up the model development process. UrbanSound8k, ESC-50 datasets, and audio data collected with our low-cost microphone are used for model development and validation. The evaluation results show that our hierarchical model, utilizing the Mel Spectrogram feature extraction method and a MobileNet model, achieves the highest accuracy of 90.18 %. Furthermore, we deploy the system and assess its performance. Our system can reliably transmit audio data with an average delay of 0.37 s, and the Raspberry Pi can perform feature extraction and classification within an average of 2.5 s. Hence, our solution offers a comprehensive and cost-effective solution to enhance noise management and control.