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ZENODO
Dataset . 2017
License: CC BY NC
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2017
License: CC BY NC
Data sources: Datacite
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Bach10 Separation Smc2017

Authors: Miron, Marius;
Abstract

The Bach10 Separation SMC2017 dataset is derived from the Bach10 dataset, which contains ten pieces of Bach chorales along the scores. We separate the audio files in the original dataset and in the dataset we synthesized with Sibelius (https://zenodo.org/record/321361#.WLW40t-i7J8), using the approaches presented in this paper: Marius Miron, Jordi Janer, Emilia Gomez, "Generating data to train convolutional neural networks for low latency classical music source separation", Sound and Music Computing Conference 2017 The dataset contains the separated audio files along the computed measures which give the quality of separation: SDR, SIR, SAR, computed with BSS Eval 3.0. For the intellectual rights and the distribution policy of the original dataset check the Bach10 dataset page: http://music.cs.northwestern.edu/data/Bach10.html The files in Bach10 Separation SMC2017 dataset are offered free of charge for non-commercial use only. You can not redistribute them nor modify them. This dataset is created by Marius Miron, Music Technology Group - Universitat Pompeu Fabra (Barcelona). This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License.

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Keywords

source separation, classical music, neural networks

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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