Singing Voice Separation via Recurrent Inference and Skip-Filtering connections

Support material and source code for the method described in : S.I. Mimilakis, K. Drossos, J.F. Santos, G. Schuller, T. Virtanen, Y. Bengio, "Monaural Singing Voice Separation with Skip-Filtering Connections and Recurrent Inference of Time-Frequency Mask", in arXiv:1711.01437 [cs.SD], Nov. 2017. This work was bumitted to ICASSP 2018.

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

Listening Examples : https://js-mim.github.io/mss_pytorch/

Requirements :

• numpy : numpy==1.13.1
• SciPy : scipy==0.19.1
• PyTorch : pytorch=='0.2.0_2'
• Other : wave(used for wav file reading), pyglet(used only for audio playback), pickle(for storing some results)
• Trained Models : TBA
• MIR_Eval : mir_eval=='0.4' (This is used only for unofficial cross-validation. For the reported evaluation please refer to: https://github.com/faroit/dsdtools)

Usage :

• Clone the repository.
• Add the base directory to your Python path.
• While "mss_pytorch" is your current directory simply execute the "processes_scripts/main_script.py" file.
• Arguments for training and testing are given to the main function of the "processes_scripts/main_script.py" file.

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.