Music instrument playing technique classification based on raw audio is a relatively unexplored area of music information retrieval research. This study systematically investigates the use of traditional audio features augmented by features based on the Hartley transform, used as input to a multiclass support vector machine (SVM) classifier, to identify up to 11 different playing techniques performed on each of the violin, viola, cello, and contrabass. Furthermore, 36- and 44-class joint instrument and playing technique classifiers were developed that achieved macro-average F-measures exceeding 0.88. Our approach expands and improves on the state-of-the-art study, which implemented sparse-coded magnitude and phase-derived spectral features.

中文翻译：

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来自原始音频的弓弦乐器演奏技巧分类

基于原始音频的乐器演奏技术分类是音乐信息检索研究中相对未开发的领域。本研究系统地研究了传统音频特征的使用，这些特征由基于哈特利变换的特征增强，用作多类支持向量机 (SVM) 分类器的输入，以识别在小提琴、中提琴、大提琴和低音提琴。此外，还开发了 36 级和 44 级联合乐器和演奏技术分类器，实现了超过 0.88 的宏观平均 F 值。我们的方法扩展并改进了最先进的研究，该研究实现了稀疏编码的幅度和相位衍生的光谱特征。