Special Issue: Measurement, Simulation, and Design of Sound in Urban Spaces

1. Introduction

Sound quality in urban spaces is not only a matter of limit values for noise levels but is also key to human physical and psychological well-being, which makes it a challenge for the latest data analysis techniques [1,2,3]. This Special Issue gathers publications on projects and achievements that, from a theoretical and practical point of view [4,5], make innovative proposals on the measurement, simulation and design of sound in urban spaces.

The interest covered refers to the contemporary field of environmental sound and soundscape research [6,7,8,9], but it also regards proposals for the monitoring and designing of outdoor public spaces [10]. The urban environment needs to be analysed using quantitative and qualitative parameters. The proposed papers highlight their holistic approaches (physical sciences, machine learning, engineering sciences, building sciences, human, and social sciences). Moreover, the Special Issue aims to gather articles in different cultural and geographical contexts, be it at the scale of a building, a district, a city and/or a megalopolis. It is hoped that this could inspire researchers and acousticians to explore new directions in this age of scientific convergence and multidisciplinary cooperation.

The Special Issue was an exciting journey for us, in which we had the opportunity to communicate with many people in the field from all over the world. Authors from Italy, Canada, and Spain—including ten universities and two acoustic firms and corporations—participated in this Special Issue. Among the numerous submissions, five successfully passed the review process.

1.1. Stabilization Time of Running Equivalent Level LAeq for Urban Road Traffic Noise

The study by Brambilla et al. investigates the stabilization time (ST) of running equivalent sound levels (L_Aeq) in urban road traffic noise [11,12]. Using a dataset from 97 sites in Milan, Italy, the research explores the time required for the L_Aeq to stabilize within a predefined uncertainty range (±0.5, ±1.0, or ±1.5 dB) [13]. ST is proposed as a more effective metric than the conventional fixed sampling times of 10–15 min typically used in noise monitoring. The study examines 268 24 h time series of 1 s L_Aeq measurements and calculates hourly stabilisation times based on these intervals. Results demonstrate significant variability in ST across different road types and traffic conditions. Notably, urban local roads exhibit the longest stabilisation times, reflecting their less consistent traffic patterns [14].

The findings suggest that using ST allows for accurate estimation of hourly L_Aeq levels tailored to varying traffic conditions, offering a more flexible and resource-efficient approach to noise monitoring. Additionally, the intermittency ratio (IR)—a metric assessing the impact of noise events—was analyzed to understand its relationship with ST and traffic flow. The study highlights that IR increases during low traffic periods, such as nighttime, thereby extending ST.

This research underscores the inadequacy of fixed sampling times in capturing urban noise dynamics and advocates for adaptive sampling based on ST and traffic characteristics. The implications are significant for urban acoustic environment studies and for improving the spatial resolution of noise monitoring systems while optimizing resources.

1.2. City Ditty: An Immersive Soundscape Sketchpad for Professionals of the Built Environment

The study by Yanaky et al. presents City Ditty, an innovative immersive soundscape design tool tailored for professionals in the built environment, such as urban planners and architects [15,16]. The study addresses the lack of accessible soundscape planning tools for these professionals, who typically lack expertise in acoustics [7,17]. By adopting a user-centered design approach, the researchers developed City Ditty to enable rapid audio-visual prototyping of urban soundscapes, promoting sound awareness and supporting design workflows. The research involved several stages: reviewing existing tools, assessing user needs through workshops, and developing an interactive prototype. The workshops revealed a need for tools that simplify soundscape integration in urban design by offering functionalities like sound manipulation, contextual sound analysis, and visualization. Using the Unity game engine, City Ditty integrates high-fidelity audio with visual models, allowing users to explore and modify soundscapes dynamically.

A sound awareness module was created to guide users through 36 tasks, providing hands-on training while teaching soundscape principles. Users could experiment with design elements such as pedestrianization, modifying construction schedules, or adding natural sound sources like bird feeders. The tool supports a dynamic exploration of contexts, enabling users to analyze soundscapes at different times of day and weather conditions. A usability study with six participants evaluated City Ditty’s interface and functionalities. Results indicated high usability and engagement, with users appreciating the tool’s immersive experience and learning potential. Challenges included improving object navigation and supporting advanced functionalities for experienced users. Participants suggested enhancements like integrating external models, expanding sound libraries, and incorporating collaborative design features.

The study concludes that City Ditty is a promising platform for democratizing soundscape planning, fostering sound-aware urban design, and advancing interdisciplinary collaboration.

1.3. NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications

The study by Díez et al. introduces NoisenseDB, a novel urban sound event database designed to improve noise monitoring applications through machine learning [18]. Existing noise monitoring systems often measure sound pressure levels but fail to identify specific noise sources, limiting their utility for implementing corrective actions [19,20]. NoisenseDB addresses this gap by providing real-world audio data that enables sound source detection and classification using advanced neural network techniques [21].

The database comprises over 1200 h of audio recordings from two urban locations in Biscay, Spain. The recordings were filtered to include meaningful sound events using a decibel threshold. The resulting dataset contains 692 annotated sound clips categorized into nine classes, including traffic noise, human voices, and natural sounds. The taxonomy organizes these into coarse and fine categories to facilitate varied levels of analysis. To ensure quality, each clip was manually labeled by a human expert.

The study evaluated three neural network architectures for sound event classification:

• Two novel convolutional neural network (CNN)-based models—one using conventional convolutional layers and the other incorporating residual networks (ResNet).
• A transformer-based Audio Spectrogram Transformer (AST) model, leveraging pretrained embeddings.

These models were trained on NoisenseDB and achieved promising results, with performance metrics such as macro-average recall highlighting the strengths of each architecture. Notably, the AST model performed well on scarce categories like “dog barking” due to transfer learning, though ResNet showed the best average performance. The study also proposed a semi-supervised learning strategy to automate labeling large datasets. This iterative approach improved classification accuracy by combining labeled and automatically predicted data, demonstrating feasibility of scaling noise monitoring systems.

NoisenseDB and its associated methodologies represent significant advancements in urban sound analysis, providing tools for better noise management in smart cities. This study emphasizes the potential of combining rich datasets with cutting-edge neural architectures to address environmental noise challenges.

1.4. Acoustic Requalification of an Urban Evolving Site and Design of a Noise Barrier: A Case Study at the Bologna Engineering School

The study by Fusaro and Garai investigates the acoustic requalification of an evolving urban site near Terracini Street in Bologna, Italy, focusing on noise pollution caused by traffic near the Engineering School of the University of Bologna [22,23]. Over the past two decades, this area has transitioned from a suburban industrial zone to a mixed-use urban environment. Noise measurements reveal that the sound pressure levels significantly exceed regulatory limits for both day and night periods, compromising the university’s acoustic environment [24].

The researchers proposed an innovative solution combining a low-noise asphalt pavement and a sonic crystal acoustic barrier (SCAB) to mitigate noise pollution [25,26,27]. Sonic crystals, composed of structured arrangements of cylindrical scatterers, offer effective noise attenuation while maintaining visual transparency and allowing air circulation [28,29]. The SCAB design considers visual and ecological impacts, reflecting the area’s identity as an academic hub.

Environmental noise was measured at various times and locations along Terracini Street, confirming noise levels consistently above the regulatory threshold of 60 dB(A) for daytime and 50 dB(A) for nighttime. Numerical and analytical modelling [30] determined the barrier’s optimal dimensions and positioning, targeting a reduction in sound levels by up to 10 dB(A) across critical frequency ranges (200–1000 Hz).

The proposed barrier system includes polymethylmethacrylate (PMMA) tubes supported by reticular metal frames, ensuring stability and durability. This intervention integrates sustainable materials and acoustic metamaterials to provide a scalable, innovative solution for similar urban settings. The research highlights the importance of combining urban planning, environmental regulations, and acoustic engineering to address noise pollution in dynamic urban landscapes.

1.5. Sonic Crystal Noise Barrier with Resonant Cavities for Train Brake Noise Mitigation

The paper by Ramírez-Solana et al. investigates sonic crystal noise barriers (SCNBs) with resonant cavities as a solution to mitigate train brake noise (TBN), a significant source of noise pollution in the frequency range of 2–4.5 kHz [31]. SCNBs, composed of periodic arrays of scatterers, Bragg and local resonance bandgaps, effectively attenuating noise in targeted frequency ranges [32,33]. The study explores the interaction between these bandgaps, focusing on the orientation of resonator cavities to optimize acoustic insulation.

Using numerical simulations via the Finite Element Method (FEM) and experimental validation with 3D-printed prototypes, the authors compare the performance of SCNBs in two configurations (0° and 90° orientations of the resonant cavities) against conventional noise barriers (CB). Numerical simulations demonstrated that SCNBs could achieve comparable or superior insulation at specific frequencies. Experimental results highlighted the critical role of scatterer orientation: the 0° SCNB provided enhanced insulation levels of up to 27 dB in the TBN target range, outperforming the CB in several scenarios. Conversely, the 90° SCNB showed reduced effectiveness due to destructive interactions between bandgap mechanisms.

The research emphasizes the potential of SCNBs as sustainable and efficient noise barriers [34]. SCNBs leverage their permeability to air and sound wave scattering properties to provide a lightweight alternative to traditional barriers. The integration of resonant cavities allows for enhanced control over acoustic performance. Despite minor limitations due to prototype scaling and manufacturing defects, the study demonstrates that SCNBs can significantly reduce perceived sound pressure levels in real-world applications, with reductions of up to 15 dB in TBN scenarios.

The findings underline the importance of precise design and orientation in SCNB implementation. Future research directions include scalability, material optimization, integration of smart technologies for adaptive noise control, and long-term durability assessments. This work provides a promising framework for addressing urban noise pollution challenges through innovative acoustic engineering solutions.

2. Conclusions

The contributions to this Special Issue collectively demonstrate a significant advancement in the understanding and application of sound measurement, simulation, and design within urban environments. The interdisciplinary nature of these works—spanning acoustics, engineering, computational modeling, and urban planning—highlights the critical role of sound in shaping modern cities, both in terms of environmental quality and human well-being. The research featured in this issue provides a comprehensive foundation for addressing complex challenges associated with urban environmental acoustics and soundscapes. By incorporating innovative methodologies such as adaptive sampling metrics, immersive soundscape tools, advanced machine learning algorithms, and cutting-edge acoustic materials, these studies collectively underscore the transformative potential of sound-related interventions. Moreover, their focus on practical applications—from noise monitoring to barrier design—bridges the gap between theoretical research and real-world implementation.

A central theme emerging from these studies is the emphasis on improving urban livability through sound management. Whether by designing tools to enhance sound awareness among urban planners, creating databases for smarter noise monitoring systems, or developing efficient noise mitigation strategies, this group of studies reflects a commitment to fostering healthier, more sustainable urban spaces. The application of advanced technologies, such as neural networks and acoustic metamaterials, further demonstrates the integration of state-of-the-art innovations in solving pressing urban challenges. Beyond their technical contributions, the studies in this issue also emphasize the importance of a holistic approach, recognizing the interplay between physical acoustics, human perception, and environmental factors. This multidisciplinary perspective enriches our understanding of sound’s role in urban dynamics and opens new avenues for collaboration across fields. The global representation of researchers and case studies also provides diverse insights, ensuring that the findings have broad applicability across various cultural and geographic contexts.

As urbanization accelerates and noise pollution becomes an increasingly urgent issue, the insights and innovations presented in this Special Issue offer valuable pathways for future research. They inspire continued exploration of smart, sustainable, and human-centered approaches to sound design in cities. Ultimately, the collective impact of these works lies in their potential to shape a new paradigm for urban soundscapes—one that balances technological advancement with the fundamental needs of human health and environmental sustainability.