Efficient Video to Audio Mapper with Visual Scene Detection
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909316924047360 |
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| author | Yi, Mingjing Li, Ming |
| author_facet | Yi, Mingjing Li, Ming |
| contents | Video-to-audio (V2A) generation aims to produce corresponding audio given silent video inputs. This task is particularly challenging due to the cross-modality and sequential nature of the audio-visual features involved. Recent works have made significant progress in bridging the domain gap between video and audio, generating audio that is semantically aligned with the video content. However, a critical limitation of these approaches is their inability to effectively recognize and handle multiple scenes within a video, often leading to suboptimal audio generation in such cases. In this paper, we first reimplement a state-of-the-art V2A model with a slightly modified light-weight architecture, achieving results that outperform the baseline. We then propose an improved V2A model that incorporates a scene detector to address the challenge of switching between multiple visual scenes. Results on VGGSound show that our model can recognize and handle multiple scenes within a video and achieve superior performance against the baseline for both fidelity and relevance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_09823 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Efficient Video to Audio Mapper with Visual Scene Detection Yi, Mingjing Li, Ming Sound Multimedia Audio and Speech Processing Video-to-audio (V2A) generation aims to produce corresponding audio given silent video inputs. This task is particularly challenging due to the cross-modality and sequential nature of the audio-visual features involved. Recent works have made significant progress in bridging the domain gap between video and audio, generating audio that is semantically aligned with the video content. However, a critical limitation of these approaches is their inability to effectively recognize and handle multiple scenes within a video, often leading to suboptimal audio generation in such cases. In this paper, we first reimplement a state-of-the-art V2A model with a slightly modified light-weight architecture, achieving results that outperform the baseline. We then propose an improved V2A model that incorporates a scene detector to address the challenge of switching between multiple visual scenes. Results on VGGSound show that our model can recognize and handle multiple scenes within a video and achieve superior performance against the baseline for both fidelity and relevance. |
| title | Efficient Video to Audio Mapper with Visual Scene Detection |
| topic | Sound Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2409.09823 |