Optimizing Feature Extraction for Symbolic Music

TL;DR

This paper introduces musif, a new feature extraction tool for symbolic music that improves classification accuracy and usability, and compares it with existing tools to identify optimal feature sets.

Contribution

The paper presents musif, a novel feature extraction tool that enhances usability and accuracy, and provides a comprehensive comparison of feature extraction methods for symbolic music.

Findings

Musif achieves comparable efficiency to existing tools.

Combining features from multiple tools yields better results.

The source code and benchmarks are publicly released.

Abstract

This paper presents a comprehensive investigation of existing feature extraction tools for symbolic music and contrasts their performance to determine the set of features that best characterizes the musical style of a given music score. In this regard, we propose a novel feature extraction tool, named musif, and evaluate its efficacy on various repertoires and file formats, including MIDI, MusicXML, and **kern. Musif approximates existing tools such as jSymbolic and music21 in terms of computational efficiency while attempting to enhance the usability for custom feature development. The proposed tool also enhances classification accuracy when combined with other sets of features. We demonstrate the contribution of each set of features and the computational resources they require. Our findings indicate that the optimal tool for feature extraction is a combination of the best features from each tool rather than those of a single one. To facilitate future research in music information retrieval, we release the source code of the tool and benchmarks.