Automatic music tagging with Harmonic CNN

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dc.contributor.author	Won, Minz
dc.contributor.author	Chun, Sangyuk
dc.contributor.author	Nieto Caballero, Oriol
dc.contributor.author	Serra, Xavier
dc.date	2019
dc.identifier.citation	Won M, Chun S, Nieto O, Serra X. Automatic music tagging with Harmonic CNN. Paper presented at: 20th International Society for Music Information Retrieval; 2019 Nov 4-8; Delft, The Netherlands.
dc.identifier.uri	http://hdl.handle.net/10230/42982
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	International Society for Music Information Retrieval (ISMIR)
dc.relation	http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset
dc.rights	© Minz Won, Sanghyuk Chun, Oriol Nieto, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Minz Won, Sanghyuk Chun, Oriol Nieto, Xavier Serra. “Automatic music tagging with Harmonic CNN”, Late Breaking/Demo at the 20th International Society for Music Information Retrieval, Delft, The Netherlands, 2019.
dc.rights	https://creativecommons.org/licenses/by/4.0/
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Automatic music tagging with Harmonic CNN
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/publishedVersion
dc.description.abstract	In this paper, we introduce the Harmonic Convolutional Neural Network (Harmonic CNN), a music representation model that exploits the inherent harmonic structure of audio signals. The proposed model outperforms previous approaches in automatic music tagging on the MagnaTagATune (MTAT) dataset.
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).

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