Summary of Melfusion: Synthesizing Music From Image and Language Cues Using Diffusion Models, by Sanjoy Chowdhury et al.
MeLFusion: Synthesizing Music from Image and Language Cues using Diffusion Models
by Sanjoy Chowdhury, Sayan Nag, K J Joseph, Balaji Vasan Srinivasan, Dinesh Manocha
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM); 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 proposed MeLFusion model is a text-to-music diffusion model that can synthesize music by effectively infusing semantics from visual cues into the generated music. Unlike previous models conditioned on textual descriptions alone, MeLFusion incorporates visual information to improve the quality of synthesized music. The model is evaluated using a new dataset called MeLBench and a proposed IMSM metric, which shows a relative gain of up to 67.98% on the FAD score. The results suggest that adding visual information to the music synthesis pipeline significantly improves the quality of generated music. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Music can express emotions and feelings in a universal way. New machine learning models can create music based on written descriptions, but what if they also use pictures? This is exactly what MeLFusion does – it takes into account both text and images to generate better music. The model uses visual cues to improve the quality of synthesized music, making it more realistic and enjoyable. |
Keywords
» Artificial intelligence » Diffusion model » Machine learning » Semantics
