mlsanityData to 14 Oct 2026

Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation

arXiv 2606.00629 ↗First posted 30 May 2026cs.SD cs.HC cs.LGcited by 0

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Abstract (arXiv)

Sound design workflows frequently oscillate between time-consuming library searches and the complexity of procedural synthesis, with practitioners typically relying on disconnected tools to address each challenge separately. This paper introduces Quality Audio Prototyping (QuAP), a working prototype that unifies content-based audio retrieval and procedural sound generation within a single interface, reducing the procedural distance between a narrative concept and its sonic realisation. QuAP integrates a similarity-based retrieval engine with real-time procedural audio models, complemented by a rule-based assistant that provides perceptually informed parameter guidance, offering definitions and recommendations derived from empirical optimisation rather than requiring prior synthesis knowledge. Preliminary evaluation confirms the viability of this approach: subjective assessment demonstrated statistically significant quality improvements in five of six embedded synthesis models, and an encoder ablation study established the preferred retrieval architecture on a sound effect dataset. A user evaluation with 16 practitioners confirmed the tool's workflow utility, with all participants agreeing that the parameter assistant preserved creative agency while lowering the barrier to procedural interaction.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 30 May 2026.

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References · 14

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  12. 2019Accurate and Scalable Version Identification Using Musically-Motivated Embeddingscited by 7
2 more
  1. 2024Improving Query-by-Vocal Imitation with Contrastive Learning and Audio Pretrainingcited by 2
  2. 2025Refining music sample identification with a self-supervised graph neural networkcited by 2

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