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 has been accepted for poster presentation at ICASSP 2018.
Please use the above citation if you find any of the code useful.
Listening Examples : https://js-mim.github.io/mss_pytorch/
- An improvement of this work, which includes a novel regularization technique, can be found here: https://github.com/dr-costas/mad-twinnet .
- Numpy : numpy==1.13.1
- SciPy : scipy==0.19.1
- PyTorch : pytorch==0.2.0_2 (For inference and model testing pytorch==0.3.0 is supported. Training needs to be checked.)
- TorchVision : torchvision==0.1.9
- Other : wave(used for wav file reading), pyglet(used only for audio playback), pickle(for storing some results)
- Trained Models : https://doi.org/10.5281/zenodo.1064805
Download and place them under "results/results_inference/"
- 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)
- 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.
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.