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.

翻译：数字声音合成为探索包含数百万种配置的广阔参数空间提供了可能。质量多样性（QD）进化算法为利用这一潜力提供了一种有前景的途径，但其成功与否取决于恰当的声学特征表示。现有的QD方法主要采用手工设计的描述符或监督分类器，这可能引入非预期的探索偏差，并将发现范围限制在熟悉的声学区域。本研究探讨了无监督降维方法，用于在QD搜索过程中自动定义并动态重构声学行为空间。我们应用主成分分析（PCA）和自编码器将高维音频特征投影到结构化网格中以实现MAP-Elites算法，并通过定期重新训练模型实现动态重构。在两个实验场景中的比较表明，自动方法在避免专家引入偏差的同时，实现了比手工行为空间显著更高的多样性。动态行为空间重构保持了进化压力并防止停滞，其中PCA在降维技术中表现出最高效性。这些成果为自动化声学发现系统提供了支持，使其能够在无需人工干预或监督训练约束的情况下探索广阔的参数空间。