标题： 其余的是沉默：利用未见物种模型进行计算音乐学 显示英文标题

标题： The Rest is Silence: Leveraging Unseen Species Models for Computational Musicology

摘要： 数十年来，音乐学家一直在创建服务于不同目的的大型数据库，以支持音乐学研究和学术工作。 随着音乐信息检索和数字音乐学等领域的兴起，现在不断有越来越多与音乐学相关的数据集和语料库出现。 然而，在历史或观察性研究环境中，这些数据集必然不完整，感兴趣集合的真实规模仍然未知——保持沉默。 在这里，我们首次将生态学中的所谓未见物种模型（USMs）应用于音乐学活动领域。 在正式介绍这些模型之后，我们通过四个案例研究展示了USMs如何应用于音乐学数据，以解决定量问题，例如：我们在RISM中遗漏了多少作曲家？ 中世纪格里高利圣咏的来源中有多少比例已经被编目？ 在不同版本的音乐印刷品中，我们预期会发现多少差异？ 歌曲在民间音乐传统各类别中的覆盖率有多大？ 最后，我们在估计大量作曲家的和声词汇量方面有多接近？ 显示英文摘要

摘要： For many decades, musicologists have engaged in creating large databases serving different purposes for musicological research and scholarship. With the rise of fields like music information retrieval and digital musicology, there is now a constant and growing influx of musicologically relevant datasets and corpora. In historical or observational settings, however, these datasets are necessarily incomplete, and the true extent of a collection of interest remains unknown -- silent. Here, we apply, for the first time, so-called Unseen Species models (USMs) from ecology to areas of musicological activity. After introducing the models formally, we show in four case studies how USMs can be applied to musicological data to address quantitative questions like: How many composers are we missing in RISM? What percentage of medieval sources of Gregorian chant have we already cataloged? How many differences in music prints do we expect to find between editions? How large is the coverage of songs from genres of a folk music tradition? And, finally, how close are we in estimating the size of the harmonic vocabulary of a large number of composers?