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

Highlights: What are the main findings? An efficient TinyML system was deployed for real-time, on-device classification of urban noise, achieving high accuracy (precision/recall up to 1.00). Heavy vehicles were identified as the most frequent noise sources, whereas aircraft generated the highest A-weighted sound pressure levels (L_a, max = 88.4 dB(A)), exceeding the local permissible limit by up to 18 dB(A). What are the implications of the main findings? The study proves that TinyML is a viable solution for dense, permanent noise monitoring networks that identify specific sources, not just volume. It enables targeted noise mitigation policies by pinpointing the most impactful contributors to urban noise pollution. Urban noise levels that exceed the World Health Organization (WHO) recommendations have become a growing concern due to their adverse effects on public health. In Bogotá, Colombia, studies by the District Department of Environment (SDA) indicate that 11.8% of the population is exposed to noise levels above the WHO limits. This research aims to identify and categorize environmental noise sources in real time using an embedded intelligent system. A total of 657 labeled audio clips were collected across eight classes and processed using a 60/20/20 train–validation–test split, ensuring that audio segments from the same continuous recording were not mixed across subsets. The system was implemented on a Raspberry Pi 2W equipped with a UMIK-1 microphone and powered by a 90 W solar panel with a 12 V battery, enabling autonomous operation. The TinyML-based model achieved precision and recall values between 0.92 and 1.00, demonstrating high performance under real urban conditions. Heavy vehicles and motorcycles accounted for the largest proportion of classified samples. Although airplane-related events were less frequent, they reached maximum sound levels of up to 88.4 dB(A), exceeding the applicable local limit of 70 dB(A) by approximately 18 dB(A) rather than by percentage. In conclusion, the results demonstrate that on-device TinyML classification is a feasible and effective strategy for urban noise monitoring. Local inference reduces latency, bandwidth usage, and privacy risks by eliminating the need to transmit raw audio to external servers. This approach provides a scalable and sustainable foundation for noise management in smart cities and supports evidence-based public policies aimed at improving urban well-being. This work presents an introductory and exploratory study on the application of TinyML for acoustic environmental monitoring, aiming to evaluate its feasibility and potential for large-scale implementation.