# Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement

arXiv: https://arxiv.org/abs/2306.01635 · HTML: https://mlsanity.com/paper/2306.01635/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2023.

## Built on by (most cited first, 7 of 7)

- [Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](https://mlsanity.com/paper/2402.17467/) (2024)
- [Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling](https://mlsanity.com/paper/2310.16334/) (2023)
- [Unifying Symbolic Music Arrangement: Track-Aware Reconstruction and Structured Tokenization](https://mlsanity.com/paper/2408.15176/) (2024)
- [SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton](https://mlsanity.com/paper/2604.25498/) (2026)
- [Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving](https://mlsanity.com/paper/2407.21030/) (2024)
- [ViTex: Visual Texture Control for Multi-Track Symbolic Music Generation via Discrete Diffusion Models](https://mlsanity.com/paper/2603.01984/) (2026)
- [UOT-IR: Structured Routing of High-Polyphony Symbolic Music into Fixed-Budget Representations](https://mlsanity.com/paper/2608.00576/) (2026)

## Abstract (arXiv)

Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines.