How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling
Summary
This paper evaluates how small adaptation interfaces (LoRA, IA3, BitFit, prefix tuning, full fine-tuning) extend a frozen Music Transformer to eleven target genres for chord-symbol time-series modeling. Results show consistent harmonic prediction improvement but limited genre identity representation, concluding that chord symbols alone are insufficient to capture complete genre identity.
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Paper page - How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling
Source: https://huggingface.co/papers/2606.07334
Abstract
Small adaptation interfaces extend a frozen Music Transformer model to multiple genres, showing consistent improvement in harmonic prediction but limited genre identity representation.
Harmony is a compact symbolic layer where mathematical pitch relations, acoustic consonance, andmusical conventionmeet. This report treatschord-symbol sequencesnot as a complete representation of music, but as an interpretable, controllable time series forgenre-local harmonic modeling. Starting from a frozen pop-jazzMusic Transformercheckpoint, I evaluate how far small adaptation interfaces can extend the model to eleven target genres: blues, bossa nova, Bach chorales, country, electronic, folk, funk, gospel, hip-hop, R&B/soul, and rock. The main evaluation comparesLoRA,IA3,BitFit,prefix tuning, andfull fine-tuningover 11 genres and 3 seeds, a complete 165-cell grid. All five methods improve over the frozen base on held-out chord prediction, with macro gains from +2.89 to +3.61 points;LoRAandIA3score highest, but Wilcoxon tests with Holm and Benjamini-Hochberg correction do not support a decisive winner. A matched-data-size control sharpens this: when genres are sub-sampled to a common corpus size,IA3stays on top butLoRA’s full-data edge disappears and it falls to last, indicating the small gaps are partly data-driven. A control-token baseline is also strong, and wrong-genre adapters often beat the frozen base, suggesting much of the effect comes from lightweight conditioning over a reusable harmonic base rather than one particular adapter family. Additional diagnostics (rank sweeps, wrong-genre rotation, a base-checkpoint ablation, chord-only genre classification, generated-output statistics, real-song evaluation, and duplicate analysis) support a bounded conclusion: chord-symbol adaptation reliably improves genre-localharmonic prediction, but chord symbols alone do not carry complete genre identity. The report therefore avoids claims about perceived genre authenticity or full musical quality, which require controlled listener or musician evaluation.
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