Machine Listening for Park Soundscape Quality Assessment
Author(s)
Boes, Michiel
Filipan, Karlo
De Coensel, Bert
Botteldooren, Dick
Griffith University Author(s)
Year published
2018
Metadata
Show full item recordAbstract
The increasing importance attributed to soundscape quality in urban design generates a need for a system for automatic quality assessment that could be used for example in monitoring. In this work, the possibility for using machine listening techniques for this purpose is explored. The outlined approach detects the presence of particular sounds in a human-inspired way, and therefore allows to draw conclusions about how soundscapes are perceived. The system proposed in this paper consists of a partly recurrent artificial neural network modified to incorporate human attention mechanisms. The network is trained on sounds recorded ...
View more >The increasing importance attributed to soundscape quality in urban design generates a need for a system for automatic quality assessment that could be used for example in monitoring. In this work, the possibility for using machine listening techniques for this purpose is explored. The outlined approach detects the presence of particular sounds in a human-inspired way, and therefore allows to draw conclusions about how soundscapes are perceived. The system proposed in this paper consists of a partly recurrent artificial neural network modified to incorporate human attention mechanisms. The network is trained on sounds recorded in typical urban parks in the city of Antwerp, and thus becomes an auditory object creation and classification system particularly tuned to this context. The system is used to analyze a continuous sound level recording in different parks, resulting in a prediction of sounds that will most likely be noticed by a park visitor. Finally, it is shown that these indicators for noticed sounds allow to construct more powerful models for soundscape quality as reported in a survey with park visitors than indicators that are more regularly used in soundscape research.
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View more >The increasing importance attributed to soundscape quality in urban design generates a need for a system for automatic quality assessment that could be used for example in monitoring. In this work, the possibility for using machine listening techniques for this purpose is explored. The outlined approach detects the presence of particular sounds in a human-inspired way, and therefore allows to draw conclusions about how soundscapes are perceived. The system proposed in this paper consists of a partly recurrent artificial neural network modified to incorporate human attention mechanisms. The network is trained on sounds recorded in typical urban parks in the city of Antwerp, and thus becomes an auditory object creation and classification system particularly tuned to this context. The system is used to analyze a continuous sound level recording in different parks, resulting in a prediction of sounds that will most likely be noticed by a park visitor. Finally, it is shown that these indicators for noticed sounds allow to construct more powerful models for soundscape quality as reported in a survey with park visitors than indicators that are more regularly used in soundscape research.
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Journal Title
ACTA ACUSTICA UNITED WITH ACUSTICA
Volume
104
Issue
1
Subject
Classical physics
Mechanical engineering
Architecture