Noise pollution must be considered to achieve sustainable cities because current levels of exposure to environmental noise are a considerable risk to the health and quality of life of citizens. Urban features and sound levels were registered in 150 streets in the Chilean cities of Talca and Valdivia to analyze the relationship between both types of variables. Urban variables related to street location, urban land use, street geometry, road traffic control, and public and private transportation showed very significant correlations with the noise levels, and multiple regression models were developed from these variables for each city. Models using only urban variables in Valdivia and Talca explained 71% and 73%, respectively, of the variability of noise. The prediction error was similar in the different types of urban roads and did not exhibit significant differences between models developed in different cities. The urban models developed in one city could, therefore, be used in other similar cities. Considering the usefulness of these variables in urban planning, these models can be a useful tool for urban planners and decision-makers to implement action plans regarding noise pollution.

Reducing environmental noise in urban settings, i.e., unwanted or harmful outdoor sounds produced by human activity, has become an important issue in recent years. Most countries have established regulations that set maximum permitted noise levels. However, enforcing these regulations effectively remains challenging as it requires active monitoring networks and audio analysis performed by trained specialists. The manual evaluation of the audio recordings is laborious, time-consuming, and inefficient since many audios exceeding the noise level threshold do not correspond to a sound event considered by the regulation. To address this challenge, this work proposes a computational pipeline to assist specialists in detecting noise sources in the built environment that do not comply with the Chilean noise regulation. The system incorporates a deep neural model following a pre-trained audio neural network architecture transferred to a dataset compiled from public sources and recordings in Valdivia, Chile. The target dataset follows a customized taxonomy of urban sound events. The system also uses a public API so potential users can post audio files to obtain a prediction matrix reporting the presence of noise sources contributing to environmental noise pollution. Experiments using recordings from two continuous noise monitoring stations showed that the amount of data to be inspected by the specialist is decreased by 97% when the deep-learning tools are used. Therefore, this system efficiently assists trained experts in enforcing noise legislation through machine-assisted environmental noise monitoring.

Noise pollution is a consequence of socioeconomic development processes and urbanization. Noise action plans, urban planning, and traffic management are costly, and their undertaking raises the issue of the value of noise reduction to households. We use hedonic pricing to estimate the value of traffic noise for urban residents in Quito, Ecuador. Results were obtained using instrumental variables in order to control the possible omitted variables due to any spatial effect. Considering a large dataset of property attributes and noise exposure, properties inside the mixed land use were compared to those right behind them in a 20-meter buffer, which was characterized by similar structural attributes but different noise exposure levels.

This paper presents the Synthetic Polyphonic Ambient Sound Source (SPASS) dataset, a publicly available synthetic polyphonic audio dataset. SPASS was designed to train deep neural networks effectively for polyphonic sound event detection (PSED) in urban soundscapes. SPASS contains synthetic recordings from five virtual environments: park, square, street, market, and waterfront. The data collection process consisted of the curation of different monophonic sound sources following a hierarchical class taxonomy, the configuration of the virtual environments with the RAVEN software library, the generation of all stimuli, and the processing of this data to create synthetic recordings of polyphonic sound events with their associated metadata. The dataset contains 5000 audio clips per environment, i.e., 25,000 stimuli of 10 s each, virtually recorded at a sampling rate of 44.1 kHz.