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Title:	Monaural score-informed source separation for classical music using convolutional neural networks
Author:	Miron, Marius; Janer Mestres, Jordi; Gómez Gutiérrez, Emilia, 1975-
Abstract:	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:	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:	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):	-Música -- Informàtica
Rights:	© 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:	Conference Object Article - Published version
Published by:	International Society for Music Information Retrieval (ISMIR)
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