Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator
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| Format: | Preprint |
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2025
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| _version_ | 1866915258585579520 |
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| author | Kang, Minjae Brandão, Martim |
| author_facet | Kang, Minjae Brandão, Martim |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_18283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator Kang, Minjae Brandão, Martim Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Sound Audio and Speech Processing 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. |
| title | Seeing Soundscapes: Audio-Visual Generation and Separation from Soundscapes Using Audio-Visual Separator |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Multimedia Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2504.18283 |