  Recently, researchers have gradually realized that in some cases, the
self-supervised pre-training on large-scale Internet data is better than that
of high-quality/manually labeled data sets, and multimodal/large models are
better than single or bimodal/small models. In this paper, we propose a robust
audio representation learning method WavBriVL based on
Bridging-Vision-and-Language (BriVL). WavBriVL projects audio, image and text
into a shared embedded space, so that multi-modal applications can be realized.
We demonstrate the qualitative evaluation of the image generated from WavBriVL
as a shared embedded space, with the main purposes of this paper:(1) Learning
the correlation between audio and image;(2) Explore a new way of image
generation, that is, use audio to generate pictures. Experimental results show
that this method can effectively generate appropriate images from audio.
