Music source sepeartion using stacked hourglass networks
This is the code for the paper 'Music source separation using stacked hourglass networks', ISMIR 2018
Check out the qualitative results here
Usage
Required packages
tensorflow, pysoundfile, librosa, bss_eval (https://github.com/craffel/mir_eval)
Dataset
MIR-1K dataset
DSD 100 dataset
Training
Set the dataset and checkpoint paths at config.py and run
python train_mir_1k.py
for MIR-1K dataset, or
python train_dsd_100.py
for DSD 100 dataset.
Evaluation
Run
python eval_mir_1k.py
for MIR-1K dataset, or
python eval_dsd_100.py
for DSD 100 dataset.
Trained models
These are the checkpoint files for each dataset to reproduce the results on the paper.