Title:
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Monaural score-informed source separation for classical music using convolutional neural networks
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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 18th International Society for Music Information Retrieval Conference (ISMIR 2017), celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina. |
Abstract:
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Score information has been shown to improve music
source separation when included into non-negative matrix
factorization (NMF) frameworks. Recently, deep learning
approaches have outperformed NMF methods in terms of
separation quality and processing time, and there is scope
to extend them with score information. In this paper, we
propose a score-informed separation system for classical
music that is based on deep learning. We propose a method
to derive training features from audio files and the corresponding
coarsely aligned scores for a set of classical music
pieces. Additionally, we introduce a convolutional neural
network architecture (CNN) with the goal of estimating
time-frequency masks for source separation. Our system is
trained with synthetic renditions derived from the original
scores and can be used to separate real-life performances
based on the same scores, provided a coarse audio-to-score
alignment. The proposed system achieves better performance
(SDR and SIR) and is less computationally intensive
than a score-informed NMF system on a dataset comprising
Bach chorales. |
Abstract:
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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 (MDM2015-0502). |
Subject(s):
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-Música -- Informàtica |
Rights:
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© Marius Miron, Jordi Janer, Emilia Gómez. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Marius Miron, Jordi Janer, Emilia Gomez. “Monaural score-informed source separation for classical music using convolutional neural networks”, 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
http://creativecommons.org/licenses/by/4.0/ |
Document type:
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Conference Object Article - Published version |
Published by:
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International Society for Music Information Retrieval (ISMIR)
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