For centuries, the history and music of Joseph Franz Haydn and Wolfgang Amadeus Mozart have been compared by scholars. Recently, the growing field of music information retrieval (MIR) has offered quantitative analyses to complement traditional qualitative analyses of these composers. In this MIR study, we classify the composer of Haydn and Mozart string quartets based on the content of their scores. Our contribution is an interpretable statistical and machine learning approach that provides high classification accuracies and musical relevance. We develop novel global features that are automatically computed from symbolic data and informed by musicological Haydn–Mozart comparative studies, particularly relating to the sonata form. Several of these proposed features are found to be important for distinguishing between Haydn and Mozart string quartets. Our Bayesian logistic regression model attains leave-one-out classification accuracies over 84%, higher than prior works and providing interpretations that could aid in assessing musicological claims. Overall, our work can help expand the longstanding dialogue surrounding Haydn and Mozart and exemplify the benefit of interpretable machine learning in MIR, with potential applications to music generation and classification of other classical composers.

中文翻译：

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海顿从哪里结束，莫扎特从哪里开始？弦乐四重奏的作曲家分类

几个世纪以来，约瑟夫·弗朗茨·海顿和沃尔夫冈·阿马德乌斯·莫扎特的历史和音乐一直被学者比较。最近，不断发展的音乐信息检索 (MIR) 领域提供了定量分析，以补充对这些作曲家的传统定性分析。在这项 MIR 研究中，我们根据乐谱内容对海顿和莫扎特弦乐四重奏的作曲家进行分类。我们的贡献是一种可解释的统计和机器学习方法，可提供高分类精度和音乐相关性。我们开发了新的全局特征，这些特征是根据符号数据自动计算的，并通过音乐学海顿-莫扎特的比较研究提供信息，特别是与奏鸣曲形式相关的研究。发现其中一些提议的特征对于区分海顿和莫扎特弦乐四重奏很重要。我们的贝叶斯逻辑回归模型实现了超过 84% 的留一法分类准确率，高于之前的作品，并提供了有助于评估音乐学主张的解释。总体而言，我们的工作可以帮助扩展围绕海顿和莫扎特的长期对话，并举例说明可解释机器学习在 MIR 中的好处，以及对其他古典作曲家的音乐生成和分类的潜在应用。