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
.
Timbre Analysis of Music Audio Signals with Convolutional Neural Networks
Title | Timbre Analysis of Music Audio Signals with Convolutional Neural Networks |
Publication Type | Conference Paper |
Year of Publication | 2017 |
Conference Name | 25th European Signal Processing Conference (EUSIPCO) |
Authors | Pons, J. , Slizovskaia O. , Gong R. , Gómez E. , & Serra X. |
Conference Start Date | 28/08/2017 |
Publisher | IEEE |
Conference Location | Kos island, Greece |
preprint/postprint document | https://arxiv.org/abs/1703.06697 |
Additional material:
Code. The code to reproduce each of the experiments is available online:
- Phoneme classification of Jingu singing: github.com/ronggong/EUSIPCO2017
- Musical instrument recognition: github.com/Veleslavia/EUSIPCO2017
- Music auto-tagging: github.com/jordipons/EUSIPCO2017
Datasets. This work was possible because several benchmarks/datasets are available for research purposes:
- Jingju a cappella singing dataset: github.com/MTG/jingjuPhonemeAnnotation
- IRMAS, a dataset for instrument recognition in musical audio signals: mtg.upf.edu/download/datasets/irmas
- MagnaTagATune dataset: mirg.city.ac.uk/codeapps/the-magnatagatune-dataset and github.com/keunwoochoi/magnatagatune-list
Slides: https://doi.org/10.5281/zenodo.884444
Video: https://youtu.be/j5D7pKa6y6o