ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
Abstract
Artificial Intelligence (AI) for music generation is undergoing rapid developments, with recent symbolic models leveraging sophisticated deep learning and diffusion model algorithms. One drawback with existing models is that they lack structural cohesion, particularly on harmonic-melodic structure. Furthermore, such existing models are largely "black-box" in nature and are not musically interpretable. This paper addresses these limitations via a novel generative music framework that incorporates concepts of Schenkerian analysis (SchA) in concert with a diffusion modeling framework. This framework, which we call ProGress (Prolongation-enhanced DiGress), adapts state-of-the-art deep models for discrete diffusion (in particular, the DiGress model of Vignac et al., 2023) for interpretable and structured music generation. Concretely, our contributions include 1) novel adaptations of the DiGress model for music generation, 2) a novel SchA-inspired phrase fusion methodology, and 3) a framework allowing users to control various aspects of the generation process to create coherent musical compositions. Results from human experiments suggest superior performance to existing state-of-the-art methods.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of ProGress, a novel framework for structured music generation that combines graph diffusion modeling with Schenkerian analysis, significantly enhancing the interpretability and coherence of AI-generated music. This work represents a meaningful advancement in the intersection of machine learning and music theory, addressing critical limitations of existing generative models.
Methodology
The proposed methodology, ProGress, integrates Schenkerian analysis with a discrete graph diffusion model to enhance music generation. The use of a structured approach to music composition, grounded in music theory, is a significant advancement over traditional black-box models. The methodology is well-detailed, providing a clear workflow from phrase generation through to fusion based on harmonic principles. However, the reliance on specific theoretical frameworks may limit its applicability across diverse musical genres.
Results & evaluation
The experiments are robust, including human evaluations and ablation studies that validate the effectiveness of the proposed model. The results indicate that ProGress outperforms existing models in terms of musical coherence and user enjoyment, which is a strong indicator of its practical utility. The sample size of 45 participants, while reasonable, may benefit from a larger cohort for more generalizable results.
Reproducibility
The paper provides sufficient implementation details, including model architecture and training parameters, which enhances reproducibility. The availability of code on GitHub is a positive aspect, allowing other researchers to replicate and build upon the work. However, the paper could benefit from clearer documentation on the dataset used and the specific preprocessing steps taken.
Limitations
One limitation is the potential overfitting to the specific structures derived from Schenkerian analysis, which might not generalize well to all musical styles. Additionally, the subjective nature of musical enjoyment can introduce variability in human evaluations, which may not fully capture the model's capabilities across different listener demographics.
Broader impact
The integration of music theory with AI has the potential to transform music composition, making it more accessible to non-experts while also providing tools for professional composers. The implications extend to various applications in music technology, including automated composition tools for film, gaming, and educational purposes. The main contribution of this paper is the introduction of ProGress, a novel framework for structured music generation that combines graph diffusion modeling with Schenkerian analysis, significantly enhancing the interpretability and coherence of AI-generated music. This work represents a meaningful advancement in the intersection of machine learning and music theory, addressing critical limitations of existing generative models.
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