Musical Phrase Segmentation via Grammatical Induction
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909212685107200 |
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| author | Perkins, Reed Ventura, Dan |
| author_facet | Perkins, Reed Ventura, Dan |
| contents | We outline a solution to the challenge of musical phrase segmentation that uses grammatical induction algorithms, a class of algorithms which infer a context-free grammar from an input sequence. We analyze the performance of five grammatical induction algorithms on three datasets using various musical viewpoint combinations. Our experiments show that the LONGESTFIRST algorithm achieves the best F1 scores across all three datasets and that input encodings that include the duration viewpoint result in the best performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Musical Phrase Segmentation via Grammatical Induction Perkins, Reed Ventura, Dan Artificial Intelligence Computation and Language We outline a solution to the challenge of musical phrase segmentation that uses grammatical induction algorithms, a class of algorithms which infer a context-free grammar from an input sequence. We analyze the performance of five grammatical induction algorithms on three datasets using various musical viewpoint combinations. Our experiments show that the LONGESTFIRST algorithm achieves the best F1 scores across all three datasets and that input encodings that include the duration viewpoint result in the best performance. |
| title | Musical Phrase Segmentation via Grammatical Induction |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2405.18742 |