New Audio Representations Image Gan Generation from BriVL

• URL: http://arxiv.org/abs/2303.04585v1
• Date: Wed, 8 Mar 2023 13:58:55 GMT
• Title: New Audio Representations Image Gan Generation from BriVL
• Authors: Sen Fang, Yangjian Wu, Bowen Gao, Teik Toe Teoh
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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