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

arXiv: https://arxiv.org/abs/2606.07334 · HTML: https://mlsanity.com/paper/2606.07334/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 5 Jun 2026.

## Builds on (most cited first, 8 of 8)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [DeepBach: a Steerable Model for Bach Chorales Generation](https://mlsanity.com/paper/1612.01010/) (2016)
- [Aligning Text-to-Music Evaluation with Human Preferences](https://mlsanity.com/paper/2503.16669/) (2025)
- [MusGO: A Community-Driven Framework For Assessing Openness in Music-Generative AI](https://mlsanity.com/paper/2507.03599/) (2025)
- [On the de-duplication of the Lakh MIDI dataset](https://mlsanity.com/paper/2509.16662/) (2025)

## Abstract (arXiv)

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.