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Intelligent Classification of Urban Noise Sources Using TinyML: Towards Efficient Noise Management in Smart Cities.

Maykol Sneyder Remolina Soto¹, Brian Amaya Guzmán¹, Pedro Antonio Aya-Parra^2,3

¹School of Science and Engineering, Universidad del Rosario, Bogotá 111711, Colombia.

Sensors (Basel, Switzerland)

|October 29, 2025

Summary

This study demonstrates Tiny Machine Learning (TinyML) is effective for real-time urban noise monitoring. The system accurately identifies noise sources like vehicles, improving smart city noise management and public health policies.

Keywords:

ML classification TinyML YAMNet acoustic models public health urban noise

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Area of Science:

Environmental Science
Acoustics
Machine Learning

Background:

Urban noise pollution exceeds World Health Organization (WHO) limits in Bogotá, affecting public health.
11.8% of Bogotá's population is exposed to noise levels above WHO recommendations.

Purpose of the Study:

To develop and evaluate an embedded intelligent system for real-time identification and categorization of environmental noise sources.
To assess the feasibility of on-device Tiny Machine Learning (TinyML) for urban acoustic monitoring.

Main Methods:

Collected and labeled 657 audio clips across eight noise classes.
Implemented a TinyML model on a Raspberry Pi 2W for autonomous, on-device audio processing.
Utilized a 60/20/20 train-validation-test split, ensuring data integrity across subsets.

Main Results:

TinyML model achieved high precision and recall (0.92-1.00) in real-world urban conditions.
Heavy vehicles and motorcycles were the most frequent noise sources.
Airplane noise events, though less frequent, reached 88.4 dB(A), significantly exceeding local limits.

Conclusions:

On-device TinyML classification is a viable and efficient method for urban noise monitoring.
Local inference minimizes latency, bandwidth, and privacy concerns, supporting scalable smart city solutions.
This approach provides a foundation for evidence-based public policy to enhance urban well-being.