  The framework of visually-guided sound source separation generally consists
of three parts: visual feature extraction, multimodal feature fusion, and sound
signal processing. An ongoing trend in this field has been to tailor involved
visual feature extractor for informative visual guidance and separately devise
module for feature fusion, while utilizing U-Net by default for sound analysis.
However, such divide-and-conquer paradigm is parameter inefficient and,
meanwhile, may obtain suboptimal performance as jointly optimizing and
harmonizing various model components is challengeable. By contrast, this paper
presents a novel approach, dubbed audio-visual predictive coding (AVPC), to
tackle this task in a parameter efficient and more effective manner. The
network of AVPC features a simple ResNet-based video analysis network for
deriving semantic visual features, and a predictive coding-based sound
separation network that can extract audio features, fuse multimodal
information, and predict sound separation masks in the same architecture. By
iteratively minimizing the prediction error between features, AVPC integrates
audio and visual information recursively, leading to progressively improved
performance. In addition, we develop a valid self-supervised learning strategy
for AVPC via co-predicting two audio-visual representations of the same sound
source. Extensive evaluations demonstrate that AVPC outperforms several
baselines in separating musical instrument sounds, while reducing the model
size significantly. Code is available at:
https://github.com/zjsong/Audio-Visual-Predictive-Coding.
