Deep learning for directional sound source separation from Ambisonics mixtures.
This respository contains code accompanying the paper "Direction Specific Ambisonics Source Separation with End-To-End Deep Learning".
For installation instructions, please see Install.md.
- Download the musdb18hq and the FUSS datasets.
- Specify the root path of each dataset in
mix.py(insideprepareMUSDB()andprepareFuss()functions). - Generate data by runninng:
python mix.py [train/validate/test] [num_mixes] [num_mixes_with_silent_sources] [minimal_angular_dist] [save_path] [optionals]
Example (Musdb18 train set with room):python mix.py train 10000 3000 5 /path/to/save/data --dataset musdb --render_room --room_size_range 2 2 1 --rt_range 0.2
Example (FUSS train set with room):python mix.py train 20000 0 5 /path/to/save/data --dataset fuss --render_room --room_size_range 2 2 1 --rt_range 0.2 - A detailed description of all configurable arguments can be found in
mix.py.
Example (Implicit mode, order 1): python train.py /path/train_dir/ /path/validate_dir/ --name o1_implicit --ambiorder 1 --ambimode implicit --checkpoints_dir ./checkpoints --batch_size 16 --use_cuda
Example (Mixed mode, order 4): python train.py /path/train_dir/ /path/validate_dir/ --name o4_mixed --ambiorder 4 --ambimode mixed --checkpoints_dir ./checkpoints --batch_size 16 --use_cuda
A detailed description of all configurable parameters can be found in train.py
Example (Implicit mode, order 2): python evaluate_music_separation.py /path/test_dir/ /checkpoint/file.pt --use_cuda --ambiorder 2 --ambimode implicit --result_dir /path/result_dir/
A detailed description of all configurable parameters can be found in evaluate_music_separation.py
We reuse code from Cone-of-Silence (https://github.com/vivjay30/Cone-of-Silence).
This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 812719.