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AI model fine-tuning explores pop/jazz mix ratios for genre-adaptive chord generation

A new paper explores how to fine-tune a music generation model for a new genre without losing proficiency in the original. Researchers studied a 25M-parameter Music Transformer, initially trained on pop music, and fine-tuned it on a smaller jazz dataset. They found that mixing in approximately 2.5K samples of the original pop data helped the model retain its pop accuracy while gaining significant jazz capabilities. AI

IMPACT This research offers insights into effective data mixing strategies for fine-tuning generative models across different domains, potentially improving co-creation tools.

RANK_REASON This is a research paper published on arXiv detailing an empirical study on model fine-tuning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI model fine-tuning explores pop/jazz mix ratios for genre-adaptive chord generation

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Jinju Lee ·

    Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation

    arXiv:2605.04998v1 Announce Type: cross Abstract: Chord progression generation is practically important but understudied. Most large-scale symbolic music systems target melody, multi-track arrangement, or audio synthesis, and chord-only models tend to be relegated to conditioning…

  2. arXiv cs.LG TIER_1 English(EN) · Jinju Lee ·

    Empirical Study of Pop and Jazz Mix Ratios for Genre-Adaptive Chord Generation

    Chord progression generation is practically important but understudied. Most large-scale symbolic music systems target melody, multi-track arrangement, or audio synthesis, and chord-only models tend to be relegated to conditioning components inside larger pipelines. This paper tr…