Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation
Jinju Lee
原文摘要
This paper treats chord progression generation as a standalone task and asks how much old-domain data must be retained in fine-tuning to acquire a new genre without forgetting the old. I study jazz fine-tuning of a pop-pretrained 25M-parameter Music Transformer (84.21% top-1 next-token accuracy on a held-out pop test set). The jazz corpus is over two orders of magnitude smaller than the pop corpus, so every run uses all 1,513 jazz training sequences, and the swept variable is the volume of pop "rehearsal" data mixed alongside: 0, 1K, 2.5K, 5K, or 10K. At the tabulated best epochs every fine-tuned run gains 7 to 9 points of jazz top-1 (the released F1 checkpoint equals the Phase 0 baseline, as the paper's Correction explains), and pop top-1 falls by 2.11 points under jazz-only fine-tuning but by at most 1.19 with rehearsal. Top-1 also scores the three header tokens of each sequence, and two of them shift whole runs: the jazz-majority runs predict the jazz genre marker even for pop songs, and most test songs carry a placeholder C-major key that a checkpoint predicts for every such song or for none, as the best epochs of F1 and F3 happen to do. Scored without the header tokens, every run gains 7.2 to 7.8 jazz points, jazz-only fine-tuning loses 1.48 pop points, and the four rehearsal volumes lose pop at nearly one rate, within 0.11 points of each other at every epoch and 0.73 to 0.90 points below baseline by their last epoch. The smallest volume tested (0.66x the jazz volume) already gives nearly all the measured protection, and no volume stops a slow decline. In informal listening the author more often selects the 10K and 1K endpoints, which carry more committed stylistic identities, than the middle of the sweep, but no formal listening study has been conducted. All six checkpoints and the selection-corrected F1 retrain ft-pop80-v2 are released at https://huggingface.co/PearlLeeStudio.
cs.SD · cs.IR · cs.LG