On the Joint Minimization of Regularization Loss Functions in Deep Variational Bayesian Methods for Attribute-Controlled Symbolic Music Generation

2025-11-10

AI Summary

In deep variational Bayesian symbolic music generation, jointly optimizing the Kullback–Leibler divergence (KLD) and attribute regularization (AR) loss remains challenging: excessive KLD constraints degrade controllability, while relaxing them compromises the standard normal prior over the latent space. To address this trade-off, we propose a joint regularization framework based on learnable attribute transformations, embedded within the variational information bottleneck paradigm. Building upon reconstruction loss, our method employs a nonlinear attribute transformation module to dynamically balance KLD and AR terms—replacing rigid linear weighting with adaptive coordination. Experiments demonstrate significant improvements in both generation quality and control accuracy across multiple continuous musical attributes (e.g., tempo, density, tonal strength), while preserving latent distributions closely approximating the standard normal. Thus, our approach achieves unified optimization of controllability and regularization.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Explicit latent variable models provide a flexible yet powerful framework for data synthesis, enabling controlled manipulation of generative factors. With latent variables drawn from a tractable probability density function that can be further constrained, these models enable continuous and semantically rich exploration of the output space by navigating their latent spaces. Structured latent representations are typically obtained through the joint minimization of regularization loss functions. In variational information bottleneck models, reconstruction loss and Kullback-Leibler Divergence (KLD) are often linearly combined with an auxiliary Attribute-Regularization (AR) loss. However, balancing KLD and AR turns out to be a very delicate matter. When KLD dominates over AR, generative models tend to lack controllability; when AR dominates over KLD, the stochastic encoder is encouraged to violate the standard normal prior. We explore this trade-off in the context of symbolic music generation with explicit control over continuous musical attributes. We show that existing approaches struggle to jointly minimize both regularization objectives, whereas suitable attribute transformations can help achieve both controllability and regularization of the target latent dimensions.

Problem

• Balancing KLD and AR losses in variational models for controllability
• Achieving joint minimization of regularization objectives in music generation
• Maintaining latent space regularization while enabling attribute control

Innovation

• Joint minimization of regularization loss functions
• Balancing KLD and attribute-regularization losses
• Using attribute transformations for latent dimension control