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Automatic Acoustic Scene Classification

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Summary:This paper presents a baseline system for automatic acoustic scene classification based on the audio signals alone. The proposed method  is derived from classic, content-based, music classification approaches, and consists in a feature extraction phase followed by two dimensionality reduction steps (PCA and LDA) and a classification phase done using a k nearest-neighbors algorithm.This paper also reports on how our system performed  in the context of the DCASE 2016 challenge, for the acoustic scene classification task.
Main Authors:Marques, Gonçalo Caetano
Other Authors:Langlois, Thibault
Subject:Machine Learning Music Information Retrieval
Year:2017
Country:Portugal
Document type:article
Access type:unknown
Associated institution:Instituto Superior de Engenharia de Lisboa
Language:English
Origin:i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers
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author Marques, Gonçalo Caetano
author2 Langlois, Thibault
author2_role author
author_facet Marques, Gonçalo Caetano
Langlois, Thibault
author_role author
country_str PT
creators_json_txt [{\"Person.name\":\"Marques, Gonçalo Caetano\"},{\"Person.name\":\"Langlois, Thibault\"}]
datacite.creators.creator.creatorName.fl_str_mv Marques, Gonçalo Caetano
Langlois, Thibault
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Machine Learning
Music Information Retrieval
datacite.titles.title.fl_str_mv Automatic Acoustic Scene Classification
dc.creator.none.fl_str_mv Marques, Gonçalo Caetano
Langlois, Thibault
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://doi.org/10.34629/ipl.isel.i-ETC.35
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv ISEL - High Institute of Engineering of Lisbon
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.source.none.fl_str_mv i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers; Vol. 3 No. 1 (2017): The CETC2016 Issue (A); ID-2
i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers; Vol. 3 N.º 1 (2017): The CETC2016 Issue (A); ID-2
2182-4010
dc.subject.none.fl_str_mv Machine Learning
Music Information Retrieval
dc.title.fl_str_mv Automatic Acoustic Scene Classification
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_6501
description This paper presents a baseline system for automatic acoustic scene classification based on the audio signals alone. The proposed method  is derived from classic, content-based, music classification approaches, and consists in a feature extraction phase followed by two dimensionality reduction steps (PCA and LDA) and a classification phase done using a k nearest-neighbors algorithm.This paper also reports on how our system performed  in the context of the DCASE 2016 challenge, for the acoustic scene classification task.
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organization_str_mv urn:organizationAcronym:isel
person_str_mv Marques, Gonçalo Caetano
Langlois, Thibault
publishDate 2017
publisher.none.fl_str_mv ISEL - High Institute of Engineering of Lisbon
repo_facet_str urn:repositoryAcronym:ietc{{{_:::_}}}i-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers
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spelling enAutomatic Acoustic Scene ClassificationMarques, Gonçalo CaetanoLanglois, ThibaultMachine LearningMusic Information RetrievalCopyright (c) 2017 Gonçalo Caetano Marqueshttp://purl.org/coar/access_right/c_abf2https://doi.org/10.34629/ipl.isel.i-ETC.35DOIhttps://journals.isel.pt/index.php/i-ETC/article/view/35URLHasVersionhttps://journals.isel.pt/index.php/i-ETC/article/view/35/22URLHasVersionhttps://doi.org/10.34629/ipl.isel.i-ETC.35DOI2017-10-26enThis paper presents a baseline system for automatic acoustic scene classification based on the audio signals alone. The proposed method  is derived from classic, content-based, music classification approaches, and consists in a feature extraction phase followed by two dimensionality reduction steps (PCA and LDA) and a classification phase done using a k nearest-neighbors algorithm.This paper also reports on how our system performed  in the context of the DCASE 2016 challenge, for the acoustic scene classification task.ISEL - High Institute of Engineering of Lisbonapplication/pdfeni-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers; Vol. 3 No. 1 (2017): The CETC2016 Issue (A); ID-2pti-ETC : ISEL Academic Journal of Electronics Telecommunications and Computers; Vol. 3 N.º 1 (2017): The CETC2016 Issue (A); ID-22182-4010engjournal articlehttp://purl.org/coar/resource_type/c_6501literatureVoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85
spellingShingle Automatic Acoustic Scene Classification
Marques, Gonçalo Caetano
Machine Learning
Music Information Retrieval
status SINGLETON
status_str VoR
subject.fl_str_mv Machine Learning
Music Information Retrieval
title Automatic Acoustic Scene Classification
title_full Automatic Acoustic Scene Classification
title_fullStr Automatic Acoustic Scene Classification
title_full_unstemmed Automatic Acoustic Scene Classification
title_short Automatic Acoustic Scene Classification
title_sort Automatic Acoustic Scene Classification
topic Machine Learning
Music Information Retrieval
topic_facet Machine Learning
Music Information Retrieval
url https://doi.org/10.34629/ipl.isel.i-ETC.35
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