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This dataset accompanies the paper: M.Miron, J.Janer,E.Gomez,"Monaural score-informed source separation for classical music using convolutional neural networks", ISMIR 2017, http://mtg.upf.edu/node/3806 The code is available at the github repository: https://github.com/MTG/DeepConvSep/tree/master/examples/bach10_scoreinformed We include the trained CNN model for the proposed approach, which can be used to separate Bach chorales with the code provided at the github repository.
deep learning, music source separation
deep learning, music source separation
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| 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. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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| downloads | 3 |

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