  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.
