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

Torija Martinez, AJ; Ruiz, DP; Ramos-Ridao, A

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
beenwidely 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 classificationmodel
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).

Citation

Torija Martinez, A., Ruiz, D., & Ramos-Ridao, A. (2014). A tool for urban soundscape evaluation applying Support Vector Machines for developing a soundscape classification model. Science of the Total Environment, 482-83, 440-451. https://doi.org/10.1016/j.scitotenv.2013.07.108

• Journal Article Type: Article
• Acceptance Date: Jul 27, 2013
• Online Publication Date: Sep 2, 2013
• Publication Date: Jun 1, 2014
• Deposit Date: Dec 3, 2019
• Journal: Science of the Total Environment
• Print ISSN: 0048-9697
• Publisher: Elsevier
• Volume: 482-83
• Pages: 440-451
• DOI: https://doi.org/10.1016/j.scitotenv.2013.07.108
• Publisher URL: https://doi.org/10.1016/j.scitotenv.2013.07.108
• Related Public URLs: https://www.sciencedirect.com/journal/science-of-the-total-environment
• Additional Information: Funders : University of Malaga and the European Commission, seventh Framework Programme for R&D of the EU, granted within the People Programme, “Co-funding of Regional, National and International Programmes” (COFUND);“Ministerio de Economía y Competitividad” of Spain; Grant Number: Agreement Grant No. 246550; Grant Number: TEC2012-38883-C02-02