aiqbench takeMachine Learning

This paper investigates the limits of adapting chord-symbol models across eleven music genres to preserve stylistic identity while improving upon frozen population baselines. The study concludes that various adaptation methods consistently outperform static bases but finds no single dominant technique, with recent updates focusing on data robustness and statistical fidelity rather than architectural breakthroughs.

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How Far Can Chord-Symbol Time-Series Adaptation Carry Genre Identity? Capabilities and Boundaries in Multi-Genre Chord-Symbol Modeling

arXivJune 16, 20261 min read

arXiv:2606.07334v3 Announce Type: replace-cross Abstract: This revision updates an 11-genre chord-symbol adaptation report. The main 165-cell result is unchanged: all methods improve over the frozen pure-pop base, with no decisive method winner. v3 adds the ft-pop80-v2 multi-seed base-restoration note and corrects a few summary statistics for exact CSV faithfulness without changing conclusions.

Abstract:This revision updates an 11-genre chord-symbol adaptation report. The main 165-cell result is unchanged: all methods improve over the frozen pure-pop base, with no decisive method winner. v3 adds the ft-pop80-v2 multi-seed base-restoration note and corrects a few summary statistics for exact CSV faithfulness without changing conclusions.

v3: ft-pop80-v2, a selection-corrected, hash-distinct jazz base, exists, reproducing over 3 seeds (top-1 75.76 +/- 0.03), so the Sec. 8 base robustness ablation is now gated by effort, not checkpoint availability. Added a v3 changelog; corrected Sec. 5.2/6.3/6.9 stats for CSV fidelity (no qualitative changes). this https URL | this https URL

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2606.07334 [cs.SD]

(or arXiv:2606.07334v3 [cs.SD] for this version)

Full text under the publisher's open licence · 112 words

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