Summary of Unsupervised Composable Representations For Audio, by Giovanni Bindi et al.
Unsupervised Composable Representations for Audio
by Giovanni Bindi, Philippe Esling
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed framework leverages an explicit compositional inductive bias to address the challenge of compositional representation learning for music data. By using a flexible auto-encoding objective that can be combined with state-of-the-art generative models, the framework demonstrates high-quality performance on unsupervised audio source separation tasks. Compared to other blind source separation methods and supervised baselines, the proposed approach achieves comparable or superior results in terms of signal-to-interference ratio metrics. Furthermore, the framework enables seamless performance of unsupervised source separation, unconditional generation, and variation generation by learning a masking diffusion model in the space of composable representations. As the proposal operates in the latent space of pre-trained neural audio codecs, it also offers lower computational costs compared to other neural baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to generate music that’s more complex and meaningful. They’ve created a system that can separate different instruments or voices from each other, even when they’re played together. This is done without any supervision or training data, just by learning patterns in the music itself. The results are impressive, with the system performing as well or better than existing methods. It’s also able to generate new music and modify existing pieces. The team hopes that this technology can be used to create new kinds of music and even help people with hearing loss. |
Keywords
» Artificial intelligence » Diffusion model » Latent space » Representation learning » Supervised » Unsupervised
