Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator

• URL: http://arxiv.org/abs/2504.18283v1
• Date: Fri, 25 Apr 2025 11:51:04 GMT
• Title: Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator
• Authors: Minjae Kang, Martim Brandão,
• Abstract summary: We propose an Audio-Visual Generation and Separation model (AV-GAS) for generating images from soundscapes.<n>Our contribution is threefold: First, we propose a new challenge in the audio-visual generation task, which is to generate an image given a multi-class audio input.<n>Second, we introduce a new audio-visual separation task, which involves generating separate images for each class present in a mixed audio input.
• Score: 3.082874165638936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent audio-visual generative models have made substantial progress in generating images from audio. However, existing approaches focus on generating images from single-class audio and fail to generate images from mixed audio. To address this, we propose an Audio-Visual Generation and Separation model (AV-GAS) for generating images from soundscapes (mixed audio containing multiple classes). Our contribution is threefold: First, we propose a new challenge in the audio-visual generation task, which is to generate an image given a multi-class audio input, and we propose a method that solves this task using an audio-visual separator. Second, we introduce a new audio-visual separation task, which involves generating separate images for each class present in a mixed audio input. Lastly, we propose new evaluation metrics for the audio-visual generation task: Class Representation Score (CRS) and a modified R@K. Our model is trained and evaluated on the VGGSound dataset. We show that our method outperforms the state-of-the-art, achieving 7% higher CRS and 4% higher R@2* in generating plausible images with mixed audio.

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