IJCAI 2024poster1 citations
Musical Phrase Segmentation via Grammatical Induction
Abstract
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.
Application domains: Music and soundMethods and resources: AI systems for ideationMethods and resources: Datasets, knowledge bases and ontologiesMethods and resources: Other methods or resourcesTheory and philosophy of arts and creativity in AI systems: Autonomous creative or artistic AI
BibTeX
@inproceedings{ijcai2024p855,
title = {Musical Phrase Segmentation via Grammatical Induction},
author = {Perkins, Reed and Ventura, Dan},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {7726--7734},
year = {2024},
month = {8},
note = {AI, Arts & Creativity},
doi = {10.24963/ijcai.2024/855},
url = {https://doi.org/10.24963/ijcai.2024/855},
}