Summary of Synthesising Handwritten Music with Gans: a Comprehensive Evaluation Of Cyclewgan, Progan, and Dcgan, by Elona Shatri et al.
Synthesising Handwritten Music with GANs: A Comprehensive Evaluation of CycleWGAN, ProGAN, and DCGAN
by Elona Shatri, Kalikidhar Palavala, George Fazekas
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 using Generative Adversarial Networks (GANs) to synthesize realistic handwritten music sheets, addressing the data scarcity problem in Optical Music Recognition (OMR) systems. The authors compare three GAN models – DCGAN, ProGAN, and CycleWGAN – for generating diverse and high-quality handwritten music images. The proposed CycleWGAN model outperforms DCGAN and ProGAN in both qualitative and quantitative evaluations, achieving superior performance in FID, IS, and KID scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to help with music recognition. Right now, we can only recognize printed music sheets, but handwritten ones are harder to digitize because of their poor quality and different handwriting styles. To solve this problem, the researchers used special computer programs called GANs to create fake handwritten music sheets that look real. They tested three different types of GANs to see which one worked best. The winner was a program called CycleWGAN, which made the most realistic and diverse handwritten music sheets. |
Keywords
» Artificial intelligence » Gan
