Explicit Tonal Tension Conditioning via Dual-Level Beam Search for Symbolic Music Generation
This work addresses the problem of fine-grained compositional control for musicians and AI music generation users, offering an incremental improvement over existing methods.
The paper tackles the challenge of explicit control over tonal tension in symbolic music generation by integrating a computational tension model into a Transformer framework, using a dual-level beam search to align generated music with desired tension curves, with objective and subjective evaluations confirming effective modulation.
State-of-the-art symbolic music generation models have recently achieved remarkable output quality, yet explicit control over compositional features, such as tonal tension, remains challenging. We propose a novel approach that integrates a computational tonal tension model, based on tonal interval vector analysis, into a Transformer framework. Our method employs a two-level beam search strategy during inference. At the token level, generated candidates are re-ranked using model probability and diversity metrics to maintain overall quality. At the bar level, a tension-based re-ranking is applied to ensure that the generated music aligns with a desired tension curve. Objective evaluations indicate that our approach effectively modulates tonal tension, and subjective listening tests confirm that the system produces outputs that align with the target tension. These results demonstrate that explicit tension conditioning through a dual-level beam search provides a powerful and intuitive tool to guide AI-generated music. Furthermore, our experiments demonstrate that our method can generate multiple distinct musical interpretations under the same tension condition.