
During February 2026, Tuomas Eerola developed a new feature for the music-computing/amads repository, focusing on enhancing music analysis capabilities. He implemented a Python module, relative_mode.py, which calculates the relative mode of musical scores using pitch-class distribution techniques. This addition improved the precision of mode analysis across diverse scores, supporting both academic research and application development. Tuomas integrated the new function into the existing analysis workflow, demonstrating disciplined, commit-driven development. His work leveraged Python programming and data processing skills to address the need for more robust tonal analysis, ultimately strengthening the library’s analytics and expanding its potential user base.
February 2026 (2026-02) monthly summary for music-computing/amads. Focused on expanding music analysis capabilities with a new relative mode calculation based on pitch-class distributions, improving precision of mode analysis across scores. Delivered a new function (relative_mode.py) and integrated it into the analysis workflow. No major bugs fixed this period. Impact: strengthens analytics capability and enables more robust tonal analysis for academic and application use cases, potentially expanding user adoption for advanced music analysis. Technologies/skills demonstrated: Python module development, data-driven music analysis, pitch-class distribution techniques, commit-driven delivery.
February 2026 (2026-02) monthly summary for music-computing/amads. Focused on expanding music analysis capabilities with a new relative mode calculation based on pitch-class distributions, improving precision of mode analysis across scores. Delivered a new function (relative_mode.py) and integrated it into the analysis workflow. No major bugs fixed this period. Impact: strengthens analytics capability and enables more robust tonal analysis for academic and application use cases, potentially expanding user adoption for advanced music analysis. Technologies/skills demonstrated: Python module development, data-driven music analysis, pitch-class distribution techniques, commit-driven delivery.

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