Exploring New Approaches in the Study of Environmental Noise in Urban Areas

A special issue of Urban Science (ISSN 2413-8851). This special issue belongs to the section "Urban Environment and Sustainability".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3503

Special Issue Editors


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Guest Editor
Acoustic Engineering Laboratory, Department of Thermal Machines and Engines, Universidad de Cadiz, C.A.S.E.M. Building, Campus, 11510 Puerto Real, Spain
Interests: environmental noise; noise mapping and action plans; navigation sciences and techniques

Special Issue Information

Dear Colleagues,

It is widely recognised that cities of the future face challenges in improving their environmental sustainability. One of these challenges is to reduce noise emissions, especially those that can be detrimental to the health of people who live and carry out their daily activities near urban noise sources. The study of environmental noise in cities appears to have reached a high level of maturity, but there are still many opportunities in this area. With this Special Issue, we aim to introduce research groups that provide innovative perspectives on urban noise. We also welcome case studies from cities that share their experiences applying environmental noise solutions.

The guiding thread of this Special Issue involves connecting the following three keywords: environmental noise, city, and new approaches. In this Special Issue, original research articles, reviews, case studies, and (short) technical notes are welcome. Research areas may include (but are not limited to) the following:

  1. Noise mapping and GIS, with special attention paid to the following:
    • New data acquisition solutions and technologies for noise mapping.
    • New perspectives using GIS for diagnosis and dissemination to the public.
    • Machine learning applications.
  2. Latest experiences in noise management and mitigation in cities, with special attention paid to the following:
    • Case studies.
    • Green technologies.
    • Walking, cycling, and public transport promotion. Is urban infrastructure suitable for modal shift?
    • Green infrastructure (trees, parks, green walls).
    • Building design.
    • New materials.
    • Smart cities applications and big data.
    • Machine learning applications.
    • Sustainable mobility, city plans, and low emission zones.
  3. Challenges in the study of environmental noise in cities from the point of view of noise, with special attention paid to the following:
    • The role of EVs in traffic noise. Is the introduction of electric vehicles into urban traffic causing a change in the prevalence of more annoying noise sources?
    • Social noise (crowds, events, nightlife, etc.). What is the acoustic balance that tourism, as an economic activity, introduces into the city?
    • Other sources of noise that might be interesting to study are welcome. Some examples could include construction activities, noise from outdoor equipment, drone noise, noise from port activities, noise related to maritime traffic and docked vessels, etc.
  4. Challenges in the study of environmental noise in cities from the point of view of noise receivers, with special attention paid to the following:
    • Behavioural studies. How do people who have to live in acoustically hostile environments modify their behaviour? How do some animal species coexist with the noise of cities?
    • Noise exposure and inequality.
    • Public awareness campaign experiences.
  5. Evidence on the importance of soundscapes and quiet zones in improving the quality of life.

We look forward to receiving your contributions.

Prof. Dr. Jose Luis Cueto Ancela
Prof. Dr. Gaetano Licitra
Guest Editors

Manuscript Submission Information

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Keywords

  • urban environmental noise
  • noise management
  • noise mapping
  • urban infrastructure
  • smart city

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Published Papers (1 paper)

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Research

27 pages, 3213 KB  
Article
Urban Sound Classification for IoT Devices in Smart City Infrastructures
by Simona Domazetovska Markovska, Viktor Gavriloski, Damjan Pecioski, Maja Anachkova, Dejan Shishkovski and Anastasija Angjusheva Ignjatovska
Urban Sci. 2025, 9(12), 517; https://doi.org/10.3390/urbansci9120517 - 5 Dec 2025
Cited by 3 | Viewed by 2861
Abstract
Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition [...] Read more.
Urban noise is a major environmental concern that affects public health and quality of life, demanding new approaches beyond conventional noise level monitoring. This study investigates the development of an AI-driven Acoustic Event Detection and Classification (AED/C) system designed for urban sound recognition and its integration into smart city application. Using the UrbanSound8K dataset, five acoustic parameters—Mel Frequency Cepstral Coefficients (MFCC), Mel Spectrogram (MS), Spectral Contrast (SC), Tonal Centroid (TC), and Chromagram (Ch)—were mathematically modeled and applied to feature extraction. Their combinations were tested with three classical machine learning algorithms: Support Vector Machines (SVM), Random Forest (RF), Naive Bayes (NB) and a deep learning approach, i.e., Convolutional Neural Networks (CNN). A total of 52 models with the three ML algorithms were analyzed along with 4 models with CNN. The MFCC-based CNN models showed the highest accuracy, achieving up to 92.68% on test data. This achieved accuracy represents approximately +2% improvement compared to prior CNN-based approaches reported in similar studies. Additionally, the number of trained models, 56 in total, exceeds those presented in comparable research, ensuring more robust performance validation and statistical reliability. Real-time validation confirmed the applicability for IoT devices, and a low-cost wireless sensor unit (WSU) was developed with fog and cloud computing for scalable data processing. The constructed WSU demonstrates a cost reduction of at least four times compared to previously developed units, while maintaining good performance, enabling broader deployment potential in smart city applications. The findings demonstrate the potential of AI-based AED/C systems for continuous, source-specific noise classification, supporting sustainable urban planning and improved environmental management in smart cities. Full article
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