# Musical Phrase Segmentation via Grammatical Induction

arXiv: https://arxiv.org/abs/2405.18742 · HTML: https://mlsanity.com/paper/2405.18742/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 51 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 2 of 2)

- [Melodic Phrase Segmentation By Deep Neural Networks](https://mlsanity.com/paper/1811.05688/) (2018)
- [Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features](https://mlsanity.com/paper/2008.07527/) (2020)

## Built on by (most cited first, 1 of 1)

- [Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music](https://mlsanity.com/paper/2509.24603/) (2025)

## Abstract (arXiv)

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.