Title:
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Data augmentation for deep learning source separation of HipHop songs
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Author:
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Martel Baro, Héctor; Miron, Marius
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Abstract:
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Comunicació presentada a: 10th International Workshop on Machine Learning and Music, celebrat el 6 d'octbure de 2017 a Barcelona. |
Abstract:
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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. |
Subject(s):
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-Music source separation -Deep learning -Hip Hop |
Rights:
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Document type:
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Conference Object Article - Accepted version |
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