Summary of Musical Phrase Segmentation Via Grammatical Induction, by Reed Perkins and Dan Ventura
Musical Phrase Segmentation via Grammatical Induction
by Reed Perkins, Dan Ventura
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 presents a solution to the challenge of musical phrase segmentation, utilizing grammatical induction algorithms that infer a context-free grammar from an input sequence. The authors analyze the performance of five such algorithms on three datasets using various musical viewpoint combinations. Notably, the LONGESTFIRST algorithm achieves the best F1 scores across all three datasets, and input encodings that include the duration viewpoint result in the best performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a problem in music analysis by using special algorithms to understand musical phrases. It compares five different algorithms on three sets of data to see which one works best. The results show that one algorithm is better than the others, and that including information about how long notes are played makes it work even better. |
