Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration

2026-06-08

AI Summary

This work addresses the limitations of existing tools and creative bottlenecks faced by composers and sound designers in timbral exploration by proposing an evolutionary generative framework that integrates Quality-Diversity (QD) algorithms with supervised discriminative models. The approach employs multi-band specialized Compositional Pattern Producing Networks (CPPNs) to reduce architectural complexity while preserving performance, and leverages the MAP-Elites algorithm to efficiently search an extended behavioral space spanning multiple duration dimensions, thereby uncovering mechanisms for cross-contextual target switching. The system autonomously generates synthetic sounds exhibiting both diversity and novelty across temporal and contextual dimensions, with its creative potential validated through an online explorer and experimental musical applications.

Abstract

This study addresses the challenges composers and sound designers face in creating and refining tools to achieve their musical goals. Using evolutionary processes to promote diversity and foster serendipitous discoveries, we automate the search through uncharted sonic spaces for sound discovery, arguing that diversity-promoting algorithms can bridge the gap between the theoretical realisation and practical accessibility of sounds. We describe a system for generative sound synthesis combining Quality Diversity (QD) algorithms with a supervised discriminative model, inspired by the Innovation Engine algorithm, and explore different configurations and the interplay between the chosen synthesis approach and the discriminative model. We examine the interaction between Compositional Pattern Producing Networks (CPPNs) and Digital Signal Processing (DSP) graphs, introducing a novel approach that uses multiple specialised CPPNs for different frequency ranges; this yields simpler networks while maintaining performance comparable to single-CPPN setups. We also investigate evolutionary stepping stones by analysing goal switches between musical and non-musical contexts, revealing how lineages traverse unlikely paths to current elites. Expanding the behaviour space of a previous study to include various sound durations, we uncover specialisation within temporal niches. Results indicate that CPPN and DSP graphs coupled with a Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a deep learning classifier can generate a substantial variety of synthetic sounds, diverse and innovative across temporal and contextual dimensions. We present the generated sound objects through an online explorer and as rendered sound files, and, in the context of music composition, an experimental application that showcases their creative potential across various durations and contexts.

Problem

• Quality-Diversity
• Sound Generation
• Innovation Engine
• Audio Exploration
• Diversity-promoting algorithms

Innovation

• Quality-Diversity
• CPPN
• DSP graphs
• Innovation Engine
• MAP-Elites