Composer Vector: Style-steering Symbolic Music Generation in a Latent Space

2026-04-03

AI Summary

Existing symbolic music generation approaches typically rely on large-scale annotated datasets and support only a single composer’s style, making fine-grained and flexible stylistic control challenging. This work proposes a training-free, inference-stage method that guides generation through manipulation of style vectors in a unified latent space, enabling—for the first time—continuous blending and precise control over multiple composers’ styles. The approach is compatible with various symbolic music generation models and, as demonstrated experimentally, effectively produces compositions in target styles while supporting smooth, interpretable style interpolation. This significantly enhances both creative flexibility and user interactivity in symbolic music generation.

Abstract

Symbolic music generation has made significant progress, yet achieving fine-grained and flexible control over composer style remains challenging. Existing training-based methods for composer style conditioning depend on large labeled datasets. Besides, these methods typically support only single-composer generation at a time, limiting their applicability to more creative or blended scenarios. In this work, we propose Composer Vector, an inference-time steering method that operates directly in the model's latent space to control composer style without retraining. Through experiments on multiple symbolic music generation models, we show that Composer Vector effectively guides generations toward target composer styles, enabling smooth and interpretable control through a continuous steering coefficient. It also enables seamless fusion of multiple styles within a unified latent space framework. Overall, our work demonstrates that simple latent space steering provides a practical and general mechanism for controllable symbolic music generation, enabling more flexible and interactive creative workflows. Code and Demo are available here: https://github.com/JiangXunyi/Composer-Vector and https://jiangxunyi.github.io/composervector.github.io/

Problem

• symbolic music generation
• composer style control
• style blending
• latent space steering
• controllable generation

Innovation

• latent space steering
• composer style control
• symbolic music generation
• style fusion
• inference-time control