js-mim / aes_wimp

Support material and source code for the system described in : "New Sonorities for Jazz Recordings: Separation and Mixing using Deep Neural Networks".

AES 2nd Workshop on Intelligent Music Production

Support material and source code for the following work: S.I. Mimilakis, E. Cano, J. Abesser, G. Schuller, "New Sonorities for Jazz Recordings: Separation and Mixing using Deep Neural Networks", in Proceedings of the 2nd AES Workshop on Intelligent Music Production, London, UK, 13 September 2016.

Please use the above citation if you find any of the code useful.

For code usage, please refer to each class. Examples are given inside method or in the "main()" call.

Requirements :

• NumPy version : '1.10.4' or later
• SciPy version : '0.17.0' or later
• cPickle version : '1.71' or later
• pyglet : For audio playback routines
• Trained Models : https://js-mim.github.io/aes_wimp/

Acknowledgements :

The research leading to these results has received funding from the European Union's H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement no.642685 MacSeNet.

aes_wimp's Issues

solo stereo and custom trained models?

Hi,

was wondering why the solo output is in mono when everything else is in stereo? Is there a way to modify this for stereo output for the solo file? I like to work with "additive components" and I can't if one is stereo and one is mono to sum up to the original mixture.

Also, I noticed you use pickle for trained models and I recently found out about pickle. However, my question here is there a way to create custom trained models with this algorithm using pickle? In other words, how exactly did you create these two pickled files? What source material is in them and what steps did you use to create them? I like to experiment and modify the way algorithms work to create new algorithms and this information would help me to create a new audio source separation algorithm.

Let me know and thanks for your time and work! Can't wait to see more!

Best regards,
Dan