Martel Baro, HéctorMiron, Marius2017-10-112017-10-112017Martel 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.http://hdl.handle.net/10230/32930Comunicació presentada a: 10th International Workshop on Machine Learning and Music, celebrat el 6 d'octbure de 2017 a Barcelona.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.application/pdfengMusic source separationDeep learningHip Hopinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess