EFLATMINOR challenges the assumptions of the standard pattern detection methodology. It exposes failures, false positives, and edge cases using the same data source. This is not a replacement for the standard analysis, but a critical examination of its limitations.
Temperaments where pattern detection gives incorrect or misleading results. These cases expose the limitations of the methodology.
Cases where the methodology breaks down: borderline standard deviations, inconsistent patterns, or temperaments that don't fit clean categories.
Cases where database labels (name, type field) contradict mathematical analysis. This reveals the gap between how temperaments are labeled and what the data actually shows.
The same temperament data can be interpreted through different theoretical lenses. Select a temperament to see multiple valid interpretations.
Common arguments from tuning theory discussions, historical sources, and alternative perspectives that challenge meantone-centric analysis.