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Monaural score-informed source separation for classical music using convolutional neural networks
| Title | Monaural score-informed source separation for classical music using convolutional neural networks |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Conference Name | 18th International Society for Music Information Retrieval Conference |
| Authors | Miron, M. , Janer J. , & Gómez E. |
| Conference Start Date | 24/10/2017 |
| Conference Location | Suzhou, China |
| preprint/postprint document | https://www.researchgate.net/publication/318637038_Monaural_score-informed_source_separation_for_classical_music_using_convolutional_neural_networks |
Additional material:
This paper follows the research reproducibility principles.
The code is available through a github repository.
We test our method with a well known classical music dataset, Bach10, which can be found online .
The training data is generated from the audio samples in the RWC instrument samples dataset with the code in the github repository.
The separated tracks, the CNN trained model and the .mat files corresponding to the results in terms of SDR,SIR,SAR can be found at the zenodo repository .
