The acoustic summary as a tool for representing urban sound environments

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Author(s)
Oldoni, Damiano
De Coensel, Bert
Bockstael, Annelies
Boes, Michiel
De Baets, Bernard
Botteldooren, Dick
Griffith University Author(s)
Year published
2015
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Show full item recordAbstract
Detecting and selecting sound events is emerging as an interesting technique for characterizing and representing the sound environment of a specific location. In this article we propose a computational model for automatically constructing a so-called acoustic summary, i.e. a comprehensive collection of sounds aiming to represent the specific sound environment at a given location. Such an acoustic summary could be used by architects, soundscape designers, and urban planners to explore – by listening – the sonic environment at a certain location as it is perceived by a human listener. The model is based on a self-organizing ...
View more >Detecting and selecting sound events is emerging as an interesting technique for characterizing and representing the sound environment of a specific location. In this article we propose a computational model for automatically constructing a so-called acoustic summary, i.e. a comprehensive collection of sounds aiming to represent the specific sound environment at a given location. Such an acoustic summary could be used by architects, soundscape designers, and urban planners to explore – by listening – the sonic environment at a certain location as it is perceived by a human listener. The model is based on a self-organizing map, a type of neural network. It starts by extracting several psychoacoustic features from the sound. A specific, extensive and unsupervised training allows this map to be tuned to the typical sounds that are likely to be heard at the microphone location. The learning algorithm takes into account some basic aspects of human perception. For example, salient events tend to be better remembered than the ones that do not stand out, even if they occur less frequently. After the training, the self-organizing map is used to form an exhaustive acoustic summary by means of automatically recording specific sound events for the microphone location. In addition to describing the proposed tool, this paper also presents a validation test with local residents in order to show the ability of the model to pick up sounds which bring out the distinctiveness and the specificity of the soundscape as a local resident would do.
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View more >Detecting and selecting sound events is emerging as an interesting technique for characterizing and representing the sound environment of a specific location. In this article we propose a computational model for automatically constructing a so-called acoustic summary, i.e. a comprehensive collection of sounds aiming to represent the specific sound environment at a given location. Such an acoustic summary could be used by architects, soundscape designers, and urban planners to explore – by listening – the sonic environment at a certain location as it is perceived by a human listener. The model is based on a self-organizing map, a type of neural network. It starts by extracting several psychoacoustic features from the sound. A specific, extensive and unsupervised training allows this map to be tuned to the typical sounds that are likely to be heard at the microphone location. The learning algorithm takes into account some basic aspects of human perception. For example, salient events tend to be better remembered than the ones that do not stand out, even if they occur less frequently. After the training, the self-organizing map is used to form an exhaustive acoustic summary by means of automatically recording specific sound events for the microphone location. In addition to describing the proposed tool, this paper also presents a validation test with local residents in order to show the ability of the model to pick up sounds which bring out the distinctiveness and the specificity of the soundscape as a local resident would do.
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Journal Title
Landscape and Urban Planning
Volume
144
Copyright Statement
© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
Subject
Environmental Sciences
Engineering
Built Environment and Design
Science & Technology
Social Sciences
Life Sciences & Biomedicine
Physical Sciences
Ecology