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

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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. DOI

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.

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

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