# Melodic Phrase Segmentation By Deep Neural Networks

arXiv: https://arxiv.org/abs/1811.05688 · HTML: https://mlsanity.com/paper/1811.05688/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 6 of 6)

- [Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](https://mlsanity.com/paper/2402.17467/) (2024)
- [Unsupervised Symbolic Music Segmentation using Ensemble Temporal Prediction Errors](https://mlsanity.com/paper/2207.00760/) (2022)
- [Musical Phrase Segmentation via Grammatical Induction](https://mlsanity.com/paper/2405.18742/) (2024)
- [Analyzing Byte-Pair Encoding on Monophonic and Polyphonic Symbolic Music: A Focus on Musical Phrase Segmentation](https://mlsanity.com/paper/2410.01448/) (2024)
- [Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music](https://mlsanity.com/paper/2509.24603/) (2025)
- [A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation](https://mlsanity.com/paper/2602.21476/) (2026)

## Abstract (arXiv)

Automated melodic phrase detection and segmentation is a classical task in content-based music information retrieval and also the key towards automated music structure analysis. However, traditional methods still cannot satisfy practical requirements. In this paper, we explore and adapt various neural network architectures to see if they can be generalized to work with the symbolic representation of music and produce satisfactory melodic phrase segmentation. The main issue of applying deep-learning methods to phrase detection is the sparse labeling problem of training sets. We proposed two tailored label engineering with corresponding training techniques for different neural networks in order to make decisions at a sequential level. Experiment results show that the CNN-CRF architecture performs the best, being able to offer finer segmentation and faster to train, while CNN, Bi-LSTM-CNN and Bi-LSTM-CRF are acceptable alternatives.