Exploring Definitions of Quality and Diversity in Sonic Measurement Spaces

2025-12-02

AI Summary

Existing quality-diversity (QD) evolutionary algorithms for digital sound synthesis rely on handcrafted features or supervised classifiers, introducing expert bias and constraining exploratory breadth. To address this, we propose an unsupervised, dynamically reconstructed behavioral space framework: acoustic features are automatically reduced in dimensionality via PCA and autoencoders; the low-dimensional embeddings serve as behavior descriptors in MAP-Elites, and the dimensionality-reduction models are periodically retrained to adapt to evolutionary dynamics. This eliminates reliance on manual priors, enabling online evolution and adaptive partitioning of behavioral representations. Experiments demonstrate that our framework significantly enhances sound diversity—particularly with PCA-based compression—effectively mitigates evolutionary stagnation, and supports large-scale, fully automated exploration of parameter spaces. It establishes a scalable, unsupervised QD optimization paradigm for generative audio synthesis.

Technology Category

• Search and Optimization: Evolutionary Computation
• Machine Learning: Evolutionary Learning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

Abstract

Digital sound synthesis presents the opportunity to explore vast parameter spaces containing millions of configurations. Quality diversity (QD) evolutionary algorithms offer a promising approach to harness this potential, yet their success hinges on appropriate sonic feature representations. Existing QD methods predominantly employ handcrafted descriptors or supervised classifiers, potentially introducing unintended exploration biases and constraining discovery to familiar sonic regions. This work investigates unsupervised dimensionality reduction methods for automatically defining and dynamically reconfiguring sonic behaviour spaces during QD search. We apply Principal Component Analysis (PCA) and autoencoders to project high-dimensional audio features onto structured grids for MAP-Elites, implementing dynamic reconfiguration through model retraining at regular intervals. Comparison across two experimental scenarios shows that automatic approaches achieve significantly greater diversity than handcrafted behaviour spaces while avoiding expert-imposed biases. Dynamic behaviour-space reconfiguration maintains evolutionary pressure and prevents stagnation, with PCA proving most effective among the dimensionality reduction techniques. These results contribute to automated sonic discovery systems capable of exploring vast parameter spaces without manual intervention or supervised training constraints.

Problem

• Automatically defining sonic behavior spaces for quality diversity algorithms
• Reducing exploration biases from handcrafted audio feature descriptors
• Enabling dynamic reconfiguration of behavior spaces during evolutionary search

Innovation

• Unsupervised dimensionality reduction for sonic behavior spaces
• Dynamic reconfiguration of behavior spaces during QD search
• PCA and autoencoders project audio features for MAP-Elites