@inproceedings{simonetta_optimizing_2023,
	address = {Milan},
	title = {Optimizing {Feature} {Extraction} for {Symbolic} {Music}},
	copyright = {All rights reserved},
	url = {https://arxiv.org/abs/2307.05107},
	abstract = {This paper presents a comprehensive investigation of existing feature extraction tools for symbolic music and contrasts their performance to determine the feature set 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 feature sets. We demonstrate the contribution of each feature set 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 a single one. To facilitate future research in music information retrieval, we release the source code of the tool and benchmarks.},
	booktitle = {Proceedings of the 24th {International} {Society} for {Music} {Information} {Retrieval} {Conference}},
	author = {Simonetta, Federico and Llorens, Ana and Serrano, Martín and García-Portugués, Eduardo and Torrente, Álvaro},
	month = nov,
	year = {2023},
	keywords = {\#nosource},
}

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