Cosmodoit: A Python Package for Adaptive, Efficient Pipelining of Feature Extraction from Performed Music

TL;DR

Cosmodoit is a Python package that streamlines and modularizes feature extraction from performed music, integrating alignment and feature extraction for efficient large-scale analysis.

Contribution

It introduces a flexible, dependency-aware pipeline that combines multiple algorithms and supports incremental updates, reducing errors and duplicated work.

Findings

Enables efficient large-scale music performance analysis

Supports multi-language algorithm integration and parameter tuning

Reduces errors and duplicated effort in feature extraction workflows

Abstract

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.