Note:
This bibliographic page is archived and will no longer be updated.
For an up-to-date list of publications from the Music Technology Group see the
Publications list
.
Data augmentation for deep learning source separation of HipHop songs
| Title | Data augmentation for deep learning source separation of HipHop songs |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Conference Name | 10th International Workshop on Machine Learning and Music |
| Authors | Martel, H. , & Miron M. |
| Conference Start Date | 06/10/2017 |
| Conference Location | Barcelona |
| Abstract | Training deep learning source separation methods involves computationally intensive procedures relying on large multi-track datasets. In this paper we use data augmentation to improve hip hop source separation using small training datasets. We analyze different training strategies and data augmentation techniques with respect to their generalization capabilities. Moreover, we propose a hip hop multi-track dataset and we implemented a web demo to demonstrate our use scenario. The evaluation is done on a part of the dataset and hip-hop songs from an external dataset. |
| preprint/postprint document | http://hdl.handle.net/10230/32930 |
Additional material:
Code available at:
https://github.com/MTG/DeepConvSep
Hip Hop dataset available at:
https://zenodo.org/record/823037
Demo available as:
https://hiphopss.github.io
