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Kim, J.H. (author), Urbano Merino, Julián (author), Liem, C.C.S. (author), Hanjalic, A. (author)
Inspired by the success of deploying deep learning in the fields of Computer Vision and Natural Language Processing, this learning paradigm has also found its way into the field of Music Information Retrieval. In order to benefit from deep learning in an effective, but also efficient manner, deep transfer learning has become a common approach...
journal article 2019
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Kim, J.H. (author), Won, Minz (author), Liem, C.C.S. (author), Hanjalic, A. (author)
In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit...
conference paper 2018
document
Kim, J.H. (author), Won, Minz (author), Serra, Xavier (author), Liem, C.C.S. (author)
The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a...
conference paper 2018