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Cosmodoit is introduced as a Python package for streamlining feature extraction from performed music by integrating performance-to-score alignment with symbolic and audio feature extraction. The package employs a modular, flexible pipeline that supports selective processing, dependency-aware computation, and incremental updates, addressing the challenge of combining algorithms across different languages and formats. Cosmodoit enables efficient large-scale processing and parameter tuning for consistent feature extraction in music performance analysis.
Stop wrestling with disparate tools and languages for music performance analysis: Cosmodoit offers a unified Python pipeline for efficient, large-scale feature extraction.
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.
