Abstract
The ecology of urban space consists not only in a certain state of natural and climatic conditions. Today, the quality of the urban environment is also determined by various aspects of sensory ecology, including the influence of sound on humans. It is customary to talk about the existence of the soundscape of the city. The article analyzes the existing approaches to the formation of the soundscape of the urban area. Modern information technologies allow automating the process of assessing the state of ecoacoustics in the city through the use of machine learning. At the same time, it is possible to control individual sound elements to form a comfortable urban environment. The authors propose the process of analyzing and processing the original sound of the city using neural networks and the study of spectrograms. These tools are used to separate audio tracks into components, for example, car sounds, construction site sounds, etc. It is also possible to form a set of sound files that take into account the changes made by urban planners. The resulting files with the sounds of the city are used in sociological studies to identify the preferences of residents. The proposed solutions are tested in the formation of the sound landscape of the areas in the city of Volgograd, Russia.
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Acknowledgments
The study has been supported by the grant from the Russian Science Foundation (RSF) and the Administration of the Volgograd Oblast (Russia) No. 22–11-20024, https://rscf.ru/en/project/22-11-20024/. The authors express gratitude to colleagues from the Department of Digital Technologies for Urban Studies, Architecture and Civil Engineering, VSTU involved in the development of the project.
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Rashevskiy, N., Parygin, D., Nazarov, K., Sinitsyn, I., Feklistov, V. (2023). Intelligent Assessment of the Acoustic Ecology of the Urban Environment. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-031-44146-2_9
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