Summary of Self-supervised Audio-visual Soundscape Stylization, by Tingle Li et al.
Self-Supervised Audio-Visual Soundscape Stylization
by Tingle Li, Renhao Wang, Po-Yao Huang, Andrew Owens, Gopala Anumanchipalli
First submitted to arxiv on: 22 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a self-supervised learning approach to manipulate speech sounds and make them sound as if they were recorded in a different scene. The model takes an audio clip from a video, applies speech enhancement, and then trains a latent diffusion model using another audio-visual clip as a conditional hint. This process allows the model to learn how to transfer sound properties from one scene to another. The paper shows that the model can be trained using unlabeled videos and that adding a visual signal improves its ability to predict sounds. The technique has potential applications in areas such as audio editing and virtual reality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making speech sounds sound like they were recorded in a different place. For example, if you record someone talking in a quiet library, the model can make it sound like they’re talking outside on a busy street. The model learns by watching videos and listening to the sounds that go with them. It’s trained using a special kind of machine learning called self-supervision, which means it figures things out without needing to be told what to do. The results are really cool and could be used in all sorts of ways, like editing movies or creating virtual reality experiences. |
Keywords
* Artificial intelligence * Diffusion model * Machine learning * Self supervised
