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[Paper] [Project page]

This repository contains code for the paper:

Andrew Owens, Alexei A. Efros. Audio-Visual Scene Analysis with Self-Supervised Multisensory Features. arXiv, 2018


This release includes code and models for:

  • On/off-screen source separation: separating the speech of an on-screen speaker from background sounds.
  • Blind source separation: audio-only source separation using u-net and PIT.
  • Sound source localization: visualizing the parts of a video that correspond to sound-making actions.
  • Self-supervised audio-visual features: a pretrained 3D CNN that can be used for downstream tasks (e.g. action recognition, source separation).


pip install tensorflow     # for CPU evaluation only
pip install tensorflow-gpu # for GPU support

We used TensorFlow version 1.8, which can be installed with:

pip install tensorflow-gpu==1.8
  • Install other python dependencies
pip install numpy matplotlib pillow scipy
  • Download the pretrained models and sample data

Pretrained audio-visual features

We have provided the features for our fused audio-visual network. These features were learned through self-supervised learning. Please see for a simple example that uses these pretrained features.

Audio-visual source separation

To try the on/off-screen source separation model, run:

python ../data/translator.mp4 --model full --duration_mult 4 --out ../results/

This will separate a speaker's voice from that of an off-screen speaker. It will write the separated video files to ../results/, and will also display them in a local webpage, for easier viewing. This produces the following videos (click to watch):

Input On-screen Off-screen

We can visually mask out one of the two on-screen speakers, thereby removing their voice:

python ../data/crossfire.mp4 --model full --mask l --out ../results/
python ../data/crossfire.mp4 --model full --mask r --out ../results/

This produces the following videos (click to watch):

Source Left Right

Blind (audio-only) source separation

This baseline trains a u-net model to minimize a permutation invariant loss.

python ../data/translator.mp4 --model unet_pit --duration_mult 4 --out ../results/

The model will write the two separated streams in an arbitrary order.

Visualizing the locations of sound sources

To view the self-supervised network's class activation map (CAM), use the --cam flag:

python ../data/translator.mp4 --model full --cam --out ../results/

This produces a video in which the CAM is overlaid as a heat map:

Action recognition and fine-tuning

We have provided example code for training an action recognition model (e.g. on the UCF-101 dataset) in This involves fine-tuning our pretrained, audio-visual network. It is also possible to train this network with only visual data (no audio).


If you use this code in your research, please consider citing our paper:

  title={Audio-Visual Scene Analysis with Self-Supervised Multisensory Features},
  author={Owens, Andrew and Efros, Alexei A},
  journal={arXiv preprint arXiv:1804.03641},


  • 11/08/18: Fixed a bug in the class activation map example code. Added Tensorflow 1.9 compatibility.


Our u-net code draws from this implementation of pix2pix.


Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features







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