🤖 AI Summary
This study addresses the lack of structured harmonic function annotations—such as tonic, dominant, and subdominant—in existing Beatles song datasets, which has hindered the integration of chord progressions with macro-level formal structure. To bridge this gap, the work introduces harmonic function theory systematically into a popular music dataset for the first time, constructing a binary annotation scheme that distinguishes stable (tonic) from unstable (subdominant and dominant) functions grounded in music theory. By combining expert annotations with structural parsing, the research establishes connections between chords and musical form at the phrase level. The resulting resource, BeatlesFC, extends the Isophonics dataset with comprehensive harmonic function labels spanning all Beatles albums, thereby filling a critical gap between chord-level and form-level analysis and offering a valuable new asset for music information retrieval and automated music analysis.
📝 Abstract
This paper presents BeatlesFC, a set of harmonic function annotations for Isophonics'The Beatles dataset. Harmonic function annotations characterize chord labels as stable (tonic) or unstable (predominant, dominant). They operate at the level of musical phrases, serving as a link between chord labels and higher-level formal structures.