Decoding the Baroque : development of a novel dataset for transformer-based harmonic analysis of flute music
Date
2025
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Abstract
The computational modeling of functional harmony in the field of Music Information Retrieval (MIR) faces significant challenges when addressing the intricate structural relationships inherent in historical styles like Baroque music. This research introduces a novel dataset of Baroque flute sonatas with detailed functional harmony annotations spanning church and chamber traditions. A specialized Transformer-based model for automatic melody harmonization is developed and evaluated on its ability to generate stylistically appropriate harmonizations across composers and forms. The study proposes that a segmentation-aware Transformer model with innovative attention mechanisms can produce stylistically coherent harmonizations while revealing tensions between period conventions and composer-specific idioms. By examining Baroque flute instrumental music beyond Bach chorales, this study addresses a critical gap in computational musicology and demonstrates how sequence-to-sequence architectures capture hierarchical structures analogous to linguistics. This thesis describes the data curation process of the Baroque flute sonata dataset, presents the enhanced Transformer model, outlines the methodology, and analyzes results concerning harmonic accuracy, stylistic coherence, and musicological implications.