ly-zhu / cof-net

Code for the paper: Visually Guided Sound Source Separation using Cascaded Opponent Filter Network

Visually guided sound source separation using cascaded opponent filter network

ACCV2020(Oral) | project

This repository contains PyTorch implementation of "Visually guided sound source separation using cascaded opponent filter network". Authors: Lingyu Zhu and Esa Rahtu. Tampere University, Finland.

Environment

Operating System: Ubuntu 18.04.4 LTS, CUDA=10.1, Python=3.7, PyTorch=1.3.0

Datasets

-The original MUSIC dataset can be downloaded from: https://github.com/roudimit/MUSIC_dataset.

-The train/val/test splits of the A-NATURAL and A-MUSIC datasets can be downloaded from link. We suggest you to download the video or audio from the original AudioSet using the provided YouTube ID in splits files.

-Please put the train/test split path in the scripts/train*.sh and scripts/eval.sh

Training

./scripts/train_sSep01_C2D_DYN.sh

Evaluation

./scripts/eval.sh

Reference

[1] Zhao, Hang, et al. "The sound of pixels." Proceedings of the European conference on computer vision (ECCV). 2018.

[2] Zhao, Hang, et al. "The sound of motions." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2019.

[3] Xu, Xudong, Bo Dai, and Dahua Lin. "Recursive visual sound separation using minus-plus net." Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 2019.

[4] Gemmeke, Jort F., et al. "Audio set: An ontology and human-labeled dataset for audio events." 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2017.

Citation

If you find this work useful in your research, please cite:

@inproceedings{zhu2020visually,
  title={Visually guided sound source separation using cascaded opponent filter network},
  author={Zhu, Lingyu and Rahtu, Esa},
  booktitle={Proceedings of the Asian Conference on Computer Vision},
  year={2020}
}

Acknowledgement

This repo is developed based on Sound-of-Pixels.

cof-net's Issues

Link to download A-MUSIC and A-NATURAL datasets?