  Segmenting objects in images and separating sound sources in audio are
challenging tasks, in part because traditional approaches require large amounts
of labeled data. In this paper we develop a neural network model for visual
object segmentation and sound source separation that learns from natural videos
through self-supervision. The model is an extension of recently proposed work
that maps image pixels to sounds. Here, we introduce a learning approach to
disentangle concepts in the neural networks, and assign semantic categories to
network feature channels to enable independent image segmentation and sound
source separation after audio-visual training on videos. Our evaluations show
that the disentangled model outperforms several baselines in semantic
segmentation and sound source separation.
