This is a PyTorch implementation of "End-to-end learning for music audio" by Dieleman and Schrauwen (2014).
My aim was mostly to learn and play with the model, and to visualise the filters, so the implementation is currently not complete. The code does not for example average predictions over a track to display AUC scores. I plan to add this later. It also uses all the tagging classes, not the top 50.
Download and prepare the data by running get_data.sh. The remaining code is in visualise.ipynb. It should produce figures such as the following:
- Dieleman, S. and Schrauwen, B., 2014, May. End-to-end learning for music audio. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6964-6968). IEEE.