Sequential pattern mining in music is a central part of automated music analysis and music generation. This paper evaluates sequential pattern mining on a corpus of Mozarabic chant neume sequences that have been annotated by a musicologist with intra-opus patterns. Significant patterns are discovered in three settings: all closed patterns, maximal closed patterns, and minimal closed patterns. Each setting is evaluated against the annotated patterns using the measures of recall and precision. The results indicate that it is possible to retrieve all known patterns with an acceptable precision using significant closed pattern discovery.

中文翻译：

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挖掘重要闭合模式的轮廓序列

音乐中的顺序模式挖掘是自动音乐分析和音乐生成的核心部分。本文评估了对 Mozarabic chant neume 序列语料库的序列模式挖掘，该语料库已由音乐学家使用作品内模式进行注释。在三种设置中发现了重要的模式：所有封闭模式、最大封闭模式和最小封闭模式。使用召回率和准确率的措施针对注释模式评估每个设置。结果表明，可以使用重要的封闭模式发现以可接受的精度检索所有已知模式。