Summary of Automatic Album Sequencing, by Vincent Herrmann et al.
Automatic Album Sequencing
by Vincent Herrmann, Dylan R. Ashley, Jürgen Schmidhuber
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multimedia (cs.MM); 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 data-driven approach to album sequencing extracts the narrative essence of independent media items, implying a technique for album production. However, this method requires advanced machine learning knowledge, limiting its accessibility. To address this, we introduce a user-friendly web-based tool that allows users to upload music tracks, execute the technique with one click, and visualize the result. Additionally, we present a new direct transformer-based album sequencing method that outperforms a random baseline but does not match the performance of the narrative essence approach. Both methods are incorporated into our web-based interface, available at this GitHub URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Album sequencing is an important part of making music albums. Researchers have developed a way to sequence songs using computers, but it’s hard for people without technical expertise to use. To make it more accessible, we created a special website that lets users upload their favorite songs and automatically generate a playlist. We also came up with a new way to sequence songs based on how well they fit together, which works pretty well. However, our new method isn’t as good as the original one developed by others. Both methods are available for anyone to try. |
Keywords
» Artificial intelligence » Machine learning » Transformer
