Títol:
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MediaEval 2018 AcousticBrainz genre task: A baseline combining deep feature embeddings across datasets
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Autor/a:
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Oramas, Sergio; Bogdanov, Dmitry; Porter, Alastair
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Abstract:
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Comunicació presentada al MediaEval 2018 Workshop celebrat a Sophia Antipolis (França) del 29 al 31 d'octubre de 2018. |
Abstract:
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In this paper we present a baseline approach for the MediaEval
2018 AcousticBrainz Genre Task that takes advantage of stacking
multiple feature embeddings learned on individual genre datasets
by simple deep learning architectures. Although we employ basic
neural networks, the combination of their deep feature embeddings
provides a significant gain in performance compared to each
individual network. |
Abstract:
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This research has received funding from the European Union’s
Horizon 2020 research and innovation programme under grant
agreements No 688382 (AudioCommons) and 770376-2 (TROMPA),
as well as the Ministry of Economy and Competitiveness of the
Spanish Government (Reference: TIN2015-69935-P). |
Drets:
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Copyright © 2018 the authors.
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Tipus de document:
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Objecte de conferència |
Publicat per:
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CEUR Workshop Proceedings
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