Summary of See-2-sound: Zero-shot Spatial Environment-to-spatial Sound, by Rishit Dagli et al.
SEE-2-SOUND: Zero-Shot Spatial Environment-to-Spatial Sound
by Rishit Dagli, Shivesh Prakash, Robert Wu, Houman Khosravani
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Sound (cs.SD); Audio and Speech Processing (eess.AS)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to generating spatial audio that complements visually generated content, such as images and videos. The SEE-2-SOUND framework decomposes the task into four stages: identifying visual regions of interest, locating these elements in 3D space, generating mono-audio for each, and integrating them into spatial audio. By applying this approach, the authors demonstrate impressive results in generating high-quality spatial audio for a variety of multimedia content, including videos, images, and dynamic images from the internet. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates special sounds that go with what you see when watching videos or looking at pictures. Right now, computers are good at making sounds that sound like people talking or music, but they’re not great at adding sounds that make it feel like you’re really there. This new method, called SEE-2-SOUND, breaks down the task into smaller steps: finding important parts in the picture, figuring out where those parts are in 3D space, making a sound for each one, and then combining them all together. The results are really cool and could be used to make videos or games more immersive. |
