# Music Boundary Detection using Convolutional Neural Networks: A comparative analysis of combined input features

arXiv: https://arxiv.org/abs/2008.07527 · HTML: https://mlsanity.com/paper/2008.07527/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods](https://mlsanity.com/paper/2303.13881/) (2023)
- [Musical Phrase Segmentation via Grammatical Induction](https://mlsanity.com/paper/2405.18742/) (2024)
- [Discovering "Words" in Music: Unsupervised Learning of Compositional Sparse Code for Symbolic Music](https://mlsanity.com/paper/2509.24603/) (2025)

## Abstract (arXiv)

The analysis of the structure of musical pieces is a task that remains a challenge for Artificial Intelligence, especially in the field of Deep Learning. It requires prior identification of structural boundaries of the music pieces. This structural boundary analysis has recently been studied with unsupervised methods and textitend-to-end techniques such as Convolutional Neural Networks (CNN) using Mel-Scaled Log-magnitude Spectograms features (MLS), Self-Similarity Matrices (SSM) or Self-Similarity Lag Matrices (SSLM) as inputs and trained with human annotations. Several studies have been published divided into unsupervised and textitend-to-end methods in which pre-processing is done in different ways, using different distance metrics and audio characteristics, so a generalized pre-processing method to compute model inputs is missing. The objective of this work is to establish a general method of pre-processing these inputs by comparing the inputs calculated from different pooling strategies, distance metrics and audio characteristics, also taking into account the computing time to obtain them. We also establish the most effective combination of inputs to be delivered to the CNN in order to establish the most efficient way to extract the limits of the structure of the music pieces. With an adequate combination of input matrices and pooling strategies we obtain a measurement accuracy F1 of 0.411 that outperforms the current one obtained under the same conditions.