RT Book, Section
T1 Optimizing feature straction for Symbolic Music
A1 Simonetta, Federico
A1 Llorens, Ana
A1 Llorens Martín, Ana
A1 Serrano, Martín
A1 García-Portugués, Eduardo
A1 Torrente, Álvaro
A1 Torrente Sánchez-Guisande, Álvaro José
A2 International Society for Music Information Retrieval, 
AB 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.
PB International Society for Music Information Retrieval
SN 978-1-7327299-3-3
YR 2023
FD 2023-03-01
LK https://hdl.handle.net/20.500.14352/114330
UL https://hdl.handle.net/20.500.14352/114330
LA eng
NO Optimizing feature straction for Symbolic Music / Fedrico Simonetta, Ana Llorens, Martín Serrano, Eduardo García-Portugué, Álvaro Torrente en :  International Society for Music Information Retrieval. (2023). Proceedings of the 24th conference of the International Society for Music Information Retrieval, November 5-9, 2023, Milan, Italy (A. Sarti, Ed.). International Society for Music Information Retrieval.
NO European Research Council
DS Docta Complutense
RD 18 abr 2025