Trajectory of Fifths in Tonal Mode Detection

The article introduces a novel algorithm designed to distinguish between the major and minor tonal modes in musical compositions. It utilizes parameters derived from the trajectory of fifths obtained for a given piece: the angular coordinates of the trajectory’s first and last points, the orientation of the trajectory’s characteristic vector, and a coefficient quantifying the imbalance in the distribution of trajectory points between quadrants associated with major and minor modes. To assess the algorithm’s effectiveness, experiments were conducted using selected sets of Bach and Chopin works transposed into C major and A minor keys, as well as on pieces from the Saarland Music Data: MIDI-Audio Piano Music in their original keys. The results indicate high detection accuracy, demonstrating the algorithm’s potential for reliable tonal mode identification.

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Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3584400
Citations
2
Primary Topic
Speech and Audio Processing
Type
article
Field-Weighted Citation Impact
2.97
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article

Trajectory of Fifths in Tonal Mode Detection

Tomasz Łukaszewicz, Dariusz Kania
2 citations
IEEE Access
Speech and Audio Processing
2.97
article

Trajectory of Fifths in Tonal Mode Detection

Tomasz Łukaszewicz, Dariusz Kania
article en
2 citations

Abstract

The article introduces a novel algorithm designed to distinguish between the major and minor tonal modes in musical compositions. It utilizes parameters derived from the trajectory of fifths obtained for a given piece: the angular coordinates of the trajectory’s first and last points, the orientation of the trajectory’s characteristic vector, and a coefficient quantifying the imbalance in the distribution of trajectory points between quadrants associated with major and minor modes. To assess the algorithm’s effectiveness, experiments were conducted using selected sets of Bach and Chopin works transposed into C major and A minor keys, as well as on pieces from the Saarland Music Data: MIDI-Audio Piano Music in their original keys. The results indicate high detection accuracy, demonstrating the algorithm’s potential for reliable tonal mode identification.

IEEE AccessVol. 13
Silesian University of Technology (PL)
Openalex Percentile: Top 9%
Speech and Audio Processing
2.97
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