Quality Audio Prototyping: a prototype system for unified sound retrieval and procedural generation
Abstract
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of QuAP, a prototype system that integrates content-based audio retrieval and procedural sound generation, thereby addressing the fragmentation in current sound design workflows. This work represents a significant advancement in audio processing, combining innovative methodologies with practical applications, and highlights the importance of user-centered design in the development of creative tools.
Methodology
The methodology employed in the development of QuAP is robust, integrating a hybrid retrieval system with procedural audio synthesis and an intelligent parameter assistant. The use of MobileNet for audio embeddings and the feature-driven bottleneck framework for optimizing synthesis parameters demonstrates a thoughtful approach to addressing the challenges in sound design workflows. However, the paper could benefit from a more detailed description of the implementation specifics and the exact parameters used in the optimization process.
Results & evaluation
The experimental evaluation is well-structured, utilizing a MUSHRA subjective evaluation to assess the quality of the synthesized audio and an ablation study to compare encoder architectures. The results indicate statistically significant improvements in sound quality for most models, which supports the effectiveness of the proposed system. However, the relatively small sample size in the user evaluation (16 participants) may limit the generalizability of the findings.
Reproducibility
While the paper provides a project URL and mentions the use of established datasets and frameworks, it lacks detailed implementation instructions or code availability, which could hinder reproducibility. More explicit documentation on the setup and execution of experiments would enhance this aspect.
Limitations
The study acknowledges limitations, particularly in the synthesis quality of certain models (e.g., Rocket and Jet) and the narrow scope of sound categories supported by QuAP. The reliance on subjective evaluations may also introduce biases, and the tool's performance in real-world scenarios remains to be fully validated.
Broader impact
QuAP has the potential to significantly impact sound design practices by streamlining workflows and enhancing creative exploration. By unifying retrieval and synthesis, it could facilitate more efficient sound design processes across various industries, including film, gaming, and music production. The focus on maintaining creative agency while providing intelligent assistance is particularly relevant in the context of increasing automation in creative fields. The main contribution of this paper is the introduction of QuAP, a prototype system that integrates content-based audio retrieval and procedural sound generation, thereby addressing the fragmentation in current sound design workflows. This work represents a significant advancement in audio processing, combining innovative methodologies with practical applications, and highlights the importance of user-centered design in the development of creative tools.
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