NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications
dc.contributor.author | Diez, Itxasne | |
dc.contributor.author | Saratxaga Couceiro, Ibon | |
dc.contributor.author | Salegi, Unai | |
dc.contributor.author | Navas Cordón, Eva | |
dc.contributor.author | Hernáez Rioja, Inmaculada | |
dc.date.accessioned | 2023-08-29T06:42:13Z | |
dc.date.available | 2023-08-29T06:42:13Z | |
dc.date.issued | 2023-08-17 | |
dc.identifier.citation | Applied Sciences 13(16) : (2023) // Article ID 9358 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10810/62246 | |
dc.description.abstract | The use of continuous monitoring systems to control aspects such as noise pollution has grown in recent years. The commercial monitoring systems used to date only provide information on noise levels but do not identify the noise sources that generate them. The identification of noise sources is an important aspect in order to apply corrective measures to mitigate the noise levels. In this sense, new technological advances like machine listening can enable the addition of other capabilities to sound monitoring systems such as the detection and classification of noise sources. Despite the increasing development of these systems, researchers have to face some shortcomings. The most frequent ones are on the one hand, the lack of data recorded in real environments and on the other hand, the need for automatic labelling of large volumes of data collected by working monitoring systems. In order to address these needs, in this paper, we present our own sound database recorded in an urban environment. Some baseline results for the database are provided using two original convolutional neural network based sound events classification systems. Additionally, a state of the art transformer-based audio classification system (AST) has been applied to obtain some baseline results. Furthermore, the database has been used for evaluating a semi-supervised strategy to train a classifier for automatic labelling that can be refined by human labellers afterwards. | es_ES |
dc.description.sponsorship | This research and software development has been supported by the Public Administration of the Autonomous Community of the Basque Country, Department of Economic Development and Infrastructure of the Basque Government, Technology and Strategy Directorate. Grants for Industrial Doctorate Training BIKAINTEK. Funding Number: 48AFW2201900008. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
dc.subject | machine listening | es_ES |
dc.subject | supervised and semi-supervised learning | es_ES |
dc.subject | noise monitoring systems | es_ES |
dc.subject | urban sounds database | es_ES |
dc.subject | sound classification | es_ES |
dc.subject | deep neural networks | es_ES |
dc.title | NoisenseDB: An Urban Sound Event Database to Develop Neural Classification Systems for Noise-Monitoring Applications | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2023-08-28T09:34:35Z | |
dc.rights.holder | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/13/16/9358 | es_ES |
dc.identifier.doi | 10.3390/app13169358 | |
dc.departamentoes | Ingeniería de comunicaciones | |
dc.departamentoeu | Komunikazioen ingeniaritza |
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Except where otherwise noted, this item's license is described as © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/).