Published September 4, 2017
| Version v1
Software
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Trained model for DSD100 stereo source separation using DeepConvSep
Description
Trained model for DSD100 stereo source separation using the two channel DeepConvSep architecture (without ILD enhancing). It can be used for separation purposes following the documentation in the repository:
https://github.com/MTG/DeepConvSep
The specific two channel architecture is described in:
Erruz, Gerard (2017). "Binaural Source Separation with Convolutional Neural Networks". Master Thesis. Music Technology Group (Universitat Pompeu Fabra, Barcelona)
Files
Files
(8.9 MB)
| Name | Size | Download all |
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md5:40b0bbf354a002637aeecf9d5b080ebe
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8.9 MB | Download |
Additional details
Related works
- Is documented by
- 10.5281/zenodo.1095834 (DOI)
References
- G. Erruz (2017). "Binaural Source Separation with Convolutional Neural Networks". Master Thesis. Music Technology Group (Universitat Pompeu Fabra, Barcelona) (to be published)
- P. Chandna, M. Miron, J. Janer, and E. Gomez, "Monoaural audio source separation using deep convolutional neural networks" International Conference on Latent Variable Analysis and Signal Separation, 2017.