Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/95172
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Panda, Renato | - |
dc.contributor.author | Paiva, Rui Pedro | - |
dc.date.accessioned | 2021-07-04T20:42:05Z | - |
dc.date.available | 2021-07-04T20:42:05Z | - |
dc.date.issued | 2011-05-13 | - |
dc.identifier.isbn | 9781617829253 | - |
dc.identifier.uri | https://hdl.handle.net/10316/95172 | - |
dc.description.abstract | In this paper we propose a solution for automatic mood tracking in audio music, based on supervised learning and classification. To this end, various music clips with a duration of 25 seconds, previously annotated with arousal and valence (AV) values, were used to train several models. These models were used to predict quadrants of the Thayer’s taxonomy and AV values, of small segments from full songs, revealing the mood changes over time. The system accuracy was measured by calculating the matching ratio between predicted results and full song annotations performed by volunteers. Different combinations of audio features, frameworks and other parameters were tested, resulting in an accuracy of 56.3% and showing there is still much room for improvement. | eng |
dc.description.sponsorship | This work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e Tecnologia - Portugal. | eng |
dc.language.iso | eng | pt |
dc.publisher | Audio Engineering Society | pt |
dc.relation | info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | Mood tracking | eng |
dc.subject | Music emotion recognition | eng |
dc.subject | Regression | eng |
dc.subject | Thayer | eng |
dc.title | Using Support Vector Machines for Automatic Mood Tracking in Audio Music | eng |
dc.type | conferenceObject | eng |
degois.publication.firstPage | 579 | pt |
degois.publication.lastPage | 586 | pt |
degois.publication.location | London, UK | pt |
degois.publication.title | 130th Audio Engineering Society Convention 2011 (AES 130) | pt |
dc.peerreviewed | yes | pt |
dc.date.embargo | 2011-05-13 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | conferenceObject | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
crisitem.project.grantno | info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.researchunit | CISUC - Centre for Informatics and Systems of the University of Coimbra | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.parentresearchunit | Faculty of Sciences and Technology | - |
crisitem.author.orcid | 0000-0003-2539-5590 | - |
crisitem.author.orcid | 0000-0003-3215-3960 | - |
Appears in Collections: | I&D CISUC - Artigos em Livros de Actas |
Files in This Item:
File | Description | Size | Format | |
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Panda, Paiva - 2011 - Using Support Vector Machines for Automatic Mood Tracking in Audio Music.pdf | 382.79 kB | Adobe PDF | View/Open |
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