Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914620350922752 |
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| author | Garcia, Nelly Bhattacharjee, Aditya Mason-Williams, Gabryel Mason-Williams, Israel Benetos, Emmanouil Reiss, Joshua |
| author_facet | Garcia, Nelly Bhattacharjee, Aditya Mason-Williams, Gabryel Mason-Williams, Israel Benetos, Emmanouil Reiss, Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00629 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation Garcia, Nelly Bhattacharjee, Aditya Mason-Williams, Gabryel Mason-Williams, Israel Benetos, Emmanouil Reiss, Joshua Sound Human-Computer Interaction Machine Learning Audio and Speech Processing 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. |
| title | Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation |
| topic | Sound Human-Computer Interaction Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2606.00629 |