Learning to Separate Object Sounds by Watching Unlabeled Video
Abstract
Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources together. We propose to learn audio-visual object models from unlabeled video, then exploit the visual context to perform audio source separation in novel videos. Our approach relies on a deep multi-instance multi-label learning framework to disentangle the audio frequency bases that map to individual visual objects, even without observing/hearing those objects in isolation. We show how the recovered disentangled bases can be used to guide audio source separation to obtain better-separated, object-level sounds. Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video. We obtain state-of-the-art results on visually-aided audio source separation and audio denoising. Our video results: http://vision.cs.utexas.edu/projects/separating_object_sounds/
Cite
@article{arxiv.1804.01665,
title = {Learning to Separate Object Sounds by Watching Unlabeled Video},
author = {Ruohan Gao and Rogerio Feris and Kristen Grauman},
journal= {arXiv preprint arXiv:1804.01665},
year = {2018}
}
Comments
Published in ECCV 2018; Project Page: http://vision.cs.utexas.edu/projects/separating_object_sounds/