Computational analysis of performed music is a key component of music information research, as performance shapes much of the music we hear. Music performance analysis studies the acoustic variations introduced by performers and how these variations reflect musical interpretation and structure. Although many algorithms and tools exist for tasks such as performance-to-score alignment and symbolic or audio feature extraction, they are spread across different programming languages and data formats, making them difficult to combine efficiently. To address this problem, we present Cosmodoit, a novel Python package designed to streamline feature extraction from performed music. Cosmodoit integrates performance-to-score alignment with symbolic and audio feature extraction in a modular, flexible pipeline that supports selective processing, dependency-aware computation, and incremental updates. Its extensible design reduces duplicated work, minimizes errors, and enables efficient large-scale processing. By accommodating algorithms implemented in multiple languages and allowing parameter tuning for consistent feature extraction, Cosmodoit provides a versatile and practical tool for both research and development in music performance analysis.

翻译：对表演音乐的计算分析是音乐信息研究的关键组成部分，因为表演塑造了我们听到的大部分音乐。音乐表演分析研究表演者引入的声音变化，以及这些变化如何反映音乐诠释与结构。尽管存在许多用于表演-乐谱对齐、符号或音频特征提取等任务的算法和工具，但它们分散在不同的编程语言和数据格式中，难以高效组合。为解决这一问题，我们提出了Cosmodoit——一个用于简化表演音乐特征提取的新型Python包。Cosmodoit以模块化、灵活的流水线方式整合表演-乐谱对齐与符号及音频特征提取，支持选择性处理、依赖感知计算和增量更新。其可扩展设计减少了重复工作，最小化错误，并支持高效的大规模处理。通过容纳以多种语言实现的算法，并允许参数调优以实现一致的特征提取，Cosmodoit为音乐表演分析的研究与开发提供了通用且实用的工具。