Data augmentation for deep learning source separation of HipHop songs
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- dc.contributor.author Martel Baro, Héctorca
- dc.contributor.author Miron, Mariusca
- dc.date.accessioned 2017-10-11T17:18:45Z
- dc.date.available 2017-10-11T17:18:45Z
- dc.date.issued 2017
- dc.description Comunicació presentada a: 10th International Workshop on Machine Learning and Music, celebrat el 6 d'octbure de 2017 a Barcelona.ca
- dc.description.abstract Training deep learning source separation methods involves computationally intensive procedures relying on large multi-track datasets. In this paper we use data augmentation to improve hip hop source sepa- ration using small training datasets. We analyze different training strate- gies and data augmentation techniques with respect to their generaliza- tion capabilities. Moreover, we propose a hip hop multi-track dataset and we implemented a web demo to demonstrate our use scenario. The evaluation is done on a part of the dataset and hip-hop songs from an external dataset.en
- dc.format.mimetype application/pdf
- dc.identifier.citation Martel H, Miron M. Data augmentation for deep learning source separation of HipHop songs. Paper presented at: 10th International Workshop on Machine Learning and Music; 2017 Oct 6; Barcelona, Spain.
- dc.identifier.uri http://hdl.handle.net/10230/32930
- dc.language.iso eng
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-70816-R
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.other Music source separationen
- dc.subject.other Deep learningen
- dc.subject.other Hip Hopen
- dc.title Data augmentation for deep learning source separation of HipHop songsca
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/acceptedVersion