Quantification of stylistic differences between musical artists is of academic interest to the music community and is also useful for other applications, such as music information retrieval and recommendation systems. Information about stylistic differences can be obtained by comparing the performances of different artists across common musical pieces. In this article we develop a statistical methodology for identifying and quantifying systematic stylistic differences among artists that are consistent across audio recordings of a common set of pieces, in terms of several musical features. Our focus is on a comparison of 10 different orchestras, based on data from audio recordings of the nine Beethoven symphonies. As generative or fully parametric models of raw audio data can be highly complex and more complex than necessary for our goal of identifying differences between orchestras, we propose to reduce the data from a set of audio recordings down to pairwise distances between orchestras, based on different musical characteristics of the recordings, such as tempo, dynamics and timbre. For each of these characteristics, we obtain multiple pairwise distance matrices, one for each movement of each symphony. We develop a hierarchical multidimensional scaling (HMDS) model to identify and quantify systematic differences between orchestras in terms of these three musical characteristics and interpret the results in the context of known qualitative information about the orchestras. This methodology is able to recover several expected systematic similarities between orchestras as well as to identify some more novel results. For example, we find that modern recordings exhibit a high degree of similarity to each other, as compared to older recordings.

中文翻译：

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分层多维缩放，用于比较音乐演奏风格

量化音乐艺术家之间的风格差异是音乐界的学术兴趣，对于其他应用程序（例如音乐信息检索和推荐系统）也很有用。有关风格差异的信息可以通过比较不同艺术家在普通音乐作品中的表演来获得。在本文中，我们开发了一种统计方法，可用于识别和量化艺术家之间系统的文体差异，这些差异在一系列常见音乐作品的录音之间，在几个音乐特征上是一致的。我们的重点是根据九个贝多芬交响曲的录音数据，对10个不同的乐团进行比较。由于原始音频数据的生成或完全参数化模型可能非常复杂，而且比我们确定乐团之间差异的目标所必需的更为复杂，因此我们建议根据不同的音频，将一组录音中的数据减少到乐团之间的成对距离录音的音乐特征，例如节奏，力度和音色。对于这些特征中的每一个，我们获得多个成对距离矩阵，每个交响乐的每次移动都对应一个矩阵。我们开发了一种层次化多维缩放（HMDS）模型，以根据这三个音乐特征来识别和量化乐团之间的系统差异，并在有关乐团的已知定性信息的背景下解释结果。这种方法论能够恢复乐团之间的一些预期的系统相似性，并能够确定一些更新颖的结果。例如，我们发现，与较早的录音相比，现代录音彼此之间具有高度相似性。