Automatic Determination of Chord Roots

TL;DR

This paper introduces a decision tree-based method that considers sequential context to accurately determine chord roots, improving upon existing models and exploring the role of harmonic and nonharmonic tones in perception.

Contribution

A novel approach using sequential features and decision trees to improve chord root detection accuracy and analyze tonal contributions.

Findings

Improved accuracy over previous models

Effective detection of nonharmonic tones

Insights into harmonic contributions to perception

Abstract

Even though chord roots constitute a fundamental concept in music theory, existing models do not explain and determine them to full satisfaction. We present a new method which takes sequential context into account to resolve ambiguities and detect nonharmonic tones. We extract features from chord pairs and use a decision tree to determine chord roots. This leads to a quantitative improvement in correctness of the predicted roots in comparison to other models. All this raises the question how much harmonic and nonharmonic tones actually contribute to the perception of chord roots.