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This research report introduces the pitchtypes library, a Python designed to address challenges in the computational modeling, analysis, and generation of musical structures. The library provides a flexible and efficient framework for representing musical structures such as pitches, intervals, chords, and harmonies, making it a versatile tool for both theoretical research and practical applications in musicology. Key features include optimized data types for performance, a modular architecture for extensibility and maintainability, and support for more advanced applications like AI-based composition and real-time music analysis. By combining traditional music theory with modern computational methods, the pitchtypes library is a tool created to contribute significantly to the field of computational musicology.
