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.

翻译：本文对现有符号音乐特征提取工具进行了全面研究，并对比其性能，以确定最能表征给定乐谱音乐风格的特征集。为此，我们提出了一款名为musif的新型特征提取工具，并在多种曲目和文件格式（包括MIDI、MusicXML和**kern）上评估其有效性。Musif在计算效率上接近jSymbolic和music21等现有工具，同时试图增强自定义特征开发的易用性。当与其他特征集结合时，该工具还能提升分类准确率。我们展示了每个特征集的贡献及其所需计算资源。研究结果表明，最优特征提取工具应融合各工具中最佳特征，而非单一工具的所有特征。为促进音乐信息检索领域的未来研究，我们公开了该工具的源代码及基准测试。