Summary of S2cap: a Benchmark and a Baseline For Singing Style Captioning, by Hyunjong Ok et al.
S2Cap: A Benchmark and a Baseline for Singing Style Captioning
by Hyunjong Ok, Jaeho Lee
First submitted to arxiv on: 15 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); 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 This paper tackles a limitation in existing audio-text datasets by creating S2Cap, a comprehensive dataset for singing voices that captures diverse vocal, acoustic, and demographic attributes. The authors develop a simple yet effective algorithm for singing style captioning, which utilizes novel techniques to mitigate misalignment between pretrained models and regularize the model’s focus on the singing voice. The proposed method outperforms state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big difference in how we understand singing voices. Right now, there aren’t many datasets that have all the details we need to study singing voices. To fix this, the authors created a new dataset called S2Cap that has lots of information about different singing voices. They also developed a special algorithm to help us better understand these voices and create new styles for them. This is important because it can be used for things like creating music or making robots that can sing. |
