Detecting sound source objects within visual observation is important for
autonomous robots to comprehend surrounding environments. Since sounding
objects have a large variety with different appearances in our living
environments, labeling all sounding objects is impossible in practice. This
calls for self-supervised learning which does not require manual labeling. Most
of conventional self-supervised learning uses monaural audio signals and images
and cannot distinguish sound source objects having similar appearances due to
poor spatial information in audio signals. To solve this problem, this paper
presents a self-supervised training method using 360{\deg} images and
multichannel audio signals. By incorporating with the spatial information in
multichannel audio signals, our method trains deep neural networks (DNNs) to
distinguish multiple sound source objects. Our system for localizing sound
source objects in the image is composed of audio and visual DNNs. The visual
DNN is trained to localize sound source candidates within an input image. The
audio DNN verifies whether each candidate actually produces sound or not. These
DNNs are jointly trained in a self-supervised manner based on a probabilistic
spatial audio model. Experimental results with simulated data showed that the
DNNs trained by our method localized multiple speakers. We also demonstrate
that the visual DNN detected objects including talking visitors and specific
exhibits from real data recorded in a science museum.

该论文介绍了一种利用 360 度图像和多通道音频信号的自监督训练方法来训练深度神经网络以区分多个声源对象的系统，旨在解决自主机器人理解周围环境的问题。