Title:
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Generating data to train convolutional neural networks for classical music
source separation
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Author:
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Miron, Marius; Janer Mestres, Jordi; Gómez Gutiérrez, Emilia, 1975-
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Abstract:
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Comunicació presentada a la conferència 14th Sound and Music Computing Conference, celebrada a Finlàndia del 5 al 8 de juliol de 2017. |
Abstract:
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Deep learning approaches have become increasingly popular
in estimating time-frequency masks for audio source
separation. However, training neural networks usually requires
a considerable amount of data. Music data is scarce,
particularly for the task of classical music source separation,
where we need multi-track recordings with isolated
instruments. In this work, we depart from the assumption
that all the renditions of a piece are based on the same musical
score, and we can generate multiple renditions of the
score by synthesizing it with different performance properties,
e.g. tempo, dynamics, timbre and local timing variations.
We then use this data to train a convolutional neural
network (CNN) which can separate with low latency all the
renditions of a score or a set of scores. The trained model
is tested on real life recordings and is able to effectively
separate the corresponding sources. This work follows the
principle of research reproducibility, providing related data
and code, and can be extended to separate other pieces. |
Abstract:
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The TITANX used for this research was donated by the
NVIDIA Corporation. This work is partially supported by
the Spanish Ministry of Economy and Competitiveness under
CASAS project (TIN2015-70816-R) and by the Spanish
Ministry of Economy and Competitiveness under the
Maria de Maeztu Units of Excellence Programme (MDM-
2015-0502). We thank Agustin Martorell for his help with
Sibelius and Pritish Chandna for his useful feedback. |
Rights:
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© 2017 Marius Miron, Jordi Janer, Emilia Gómez. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
https://creativecommons.org/licenses/by/3.0/
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Document type:
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Conference Object Article - Accepted version |
Published by:
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Aalto University
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