The MTG-Jamendo dataset for automatic music tagging

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dc.contributor.author	Bogdanov, Dmitry
dc.contributor.author	Won, Minz
dc.contributor.author	Tovstogan, Philip
dc.contributor.author	Porter, Alastair
dc.contributor.author	Serra, Xavier
dc.date	2019
dc.identifier.citation	Bogdanov D, Won M, Tovstogan P, Porter A, Serra X. The MTG-Jamendo dataset for automatic music tagging. Paper presented at: ML4MD Machine Learning for Music Discovery Workshop at ICML2019; 2019 Jun 15; Long Beach, California.
dc.identifier.uri	http://hdl.handle.net/10230/42015
dc.format	application/pdf
dc.language.iso	eng
dc.relation	https://mtg.github.io/acousticbrainz-genre-dataset/#downloads
dc.relation	info:eu-repo/grantAgreement/EC/H2020/765068
dc.relation	info:eu-repo/grantAgreement/EC/H2020/688382
dc.rights	Aquest document està subjecte a una llicència Creative Commons
dc.rights	https://creativecommons.org/licenses/by/3.0/es/
dc.rights	info:eu-repo/semantics/openAccess
dc.title	The MTG-Jamendo dataset for automatic music tagging
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	Comunicació presentada a: ML4MD Machine Learning for Music Discovery Workshop del congrés ICML2019 celebrat el 15 de juny de 2019 a Long Beach, California.
dc.description.abstract	We present the MTG-Jamendo Dataset, a new open dataset for music auto-tagging. It is built using music available at Jamendo under Creative Commons licenses and tags provided by content uploaders. The dataset contains over 55,000 full audio tracks with 195 tags from genre, instrument, and mood/theme categories. We provide elaborated data splits for researchers and report the performance of a simple baseline approach on five different sets of tags: genre, instrument, mood/theme, top-50, and overall.
dc.description.abstract	This work was funded by the predoctoral grant MDM-2015- 0502-17-2 from the Spanish Ministry of Economy and Competitiveness linked to the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 765068. This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 688382 “AudioCommons”.

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