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A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model

A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model
A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model
To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).
soundscape classifier, classification model, acoustical assessment, soundscape evaluation, support vector machines, sequential minimal optimization
0048-9697
440-451
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
Ramos-Ridao, Ángel F.
0d4b4ea4-3443-4a0a-aab7-1687827fecba
Torija, Antonio J.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ruiz, Diego P.
ab9eb00f-171c-417f-8304-5105e41cbd03
Ramos-Ridao, Ángel F.
0d4b4ea4-3443-4a0a-aab7-1687827fecba

Torija, Antonio J., Ruiz, Diego P. and Ramos-Ridao, Ángel F. (2014) A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model. Science of the Total Environment, 482-483, 440-451. (doi:10.1016/j.scitotenv.2013.07.108).

Record type: Article

Abstract

To ensure appropriate soundscape management in urban environments, the urban-planning authorities need a range of tools that enable such a task to be performed. An essential step during the management of urban areas from a sound standpoint should be the evaluation of the soundscape in such an area. In this sense, it has been widely acknowledged that a subjective and acoustical categorization of a soundscape is the first step to evaluate it, providing a basis for designing or adapting it to match people's expectations as well. In this sense, this work proposes a model for automatic classification of urban soundscapes. This model is intended for the automatic classification of urban soundscapes based on underlying acoustical and perceptual criteria. Thus, this classification model is proposed to be used as a tool for a comprehensive urban soundscape evaluation. Because of the great complexity associated with the problem, two machine learning techniques, Support Vector Machines (SVM) and Support Vector Machines trained with Sequential Minimal Optimization (SMO), are implemented in developing model classification. The results indicate that the SMO model outperforms the SVM model in the specific task of soundscape classification. With the implementation of the SMO algorithm, the classification model achieves an outstanding performance (91.3% of instances correctly classified).

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More information

Accepted/In Press date: 27 July 2013
e-pub ahead of print date: 2 September 2013
Published date: 1 June 2014
Keywords: soundscape classifier, classification model, acoustical assessment, soundscape evaluation, support vector machines, sequential minimal optimization
Organisations: Acoustics Group

Identifiers

Local EPrints ID: 386673
URI: http://eprints.soton.ac.uk/id/eprint/386673
ISSN: 0048-9697
PURE UUID: 9ede5862-c32b-4461-948c-639ddc7c63ce
ORCID for Antonio J. Torija: ORCID iD orcid.org/0000-0002-5915-3736

Catalogue record

Date deposited: 03 Feb 2016 12:19
Last modified: 14 Mar 2024 22:35

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Contributors

Author: Antonio J. Torija ORCID iD
Author: Diego P. Ruiz
Author: Ángel F. Ramos-Ridao

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