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SonicRAG : High Fidelity Sound Effects Synthesis Based on Retrival Augmented Generation

Yu-Ren Guo, Wen-Kai Tai

TL;DR

The paper tackles high-fidelity sound effects synthesis under data scarcity and temporal modeling challenges by introducing SonicRAG, a retrieval-augmented framework that leverages LLMs to analyze natural language prompts, retrieve relevant audio assets, and orchestrate synthesis through a structured Mixer Script. Key contributions include the Mixer Script abstraction, a compact sound-event metadata schema, a context-aware retrieval policy, and a formal Markov decision process framework that guides iterative generation from retrieved assets. The approach demonstrates competitive quality and flexibility against baselines, while reducing the need for extensive retraining and enabling an interactive, designer-oriented workflow. Practically, SonicRAG bridges retrieval-based and generative methods to enable scalable, high-quality AI-assisted sound design for multimedia production.

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing (NLP) and multimodal learning, with successful applications in text generation and speech synthesis, enabling a deeper understanding and generation of multimodal content. In the field of sound effects (SFX) generation, LLMs have been leveraged to orchestrate multiple models for audio synthesis. However, due to the scarcity of annotated datasets, and the complexity of temproal modeling. current SFX generation techniques still fall short in achieving high-fidelity audio. To address these limitations, this paper introduces a novel framework that integrates LLMs with existing sound effect databases, allowing for the retrieval, recombination, and synthesis of audio based on user requirements. By leveraging this approach, we enhance the diversity and quality of generated sound effects while eliminating the need for additional recording costs, offering a flexible and efficient solution for sound design and application.

SonicRAG : High Fidelity Sound Effects Synthesis Based on Retrival Augmented Generation

TL;DR

The paper tackles high-fidelity sound effects synthesis under data scarcity and temporal modeling challenges by introducing SonicRAG, a retrieval-augmented framework that leverages LLMs to analyze natural language prompts, retrieve relevant audio assets, and orchestrate synthesis through a structured Mixer Script. Key contributions include the Mixer Script abstraction, a compact sound-event metadata schema, a context-aware retrieval policy, and a formal Markov decision process framework that guides iterative generation from retrieved assets. The approach demonstrates competitive quality and flexibility against baselines, while reducing the need for extensive retraining and enabling an interactive, designer-oriented workflow. Practically, SonicRAG bridges retrieval-based and generative methods to enable scalable, high-quality AI-assisted sound design for multimedia production.

Abstract

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing (NLP) and multimodal learning, with successful applications in text generation and speech synthesis, enabling a deeper understanding and generation of multimodal content. In the field of sound effects (SFX) generation, LLMs have been leveraged to orchestrate multiple models for audio synthesis. However, due to the scarcity of annotated datasets, and the complexity of temproal modeling. current SFX generation techniques still fall short in achieving high-fidelity audio. To address these limitations, this paper introduces a novel framework that integrates LLMs with existing sound effect databases, allowing for the retrieval, recombination, and synthesis of audio based on user requirements. By leveraging this approach, we enhance the diversity and quality of generated sound effects while eliminating the need for additional recording costs, offering a flexible and efficient solution for sound design and application.
Paper Structure (20 sections, 2 equations, 5 figures, 3 tables)

This paper contains 20 sections, 2 equations, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Releated Work
  3. Audio Generation
  4. Assistants Based on LLMs
  5. Prompt Engineering
  6. Retrieval-Augmented Generation
  7. Methodology
  8. Mixer Script
  9. Metadata of Sound Events
  10. Context-Aware Retrieval
  11. Framework
  12. Experiments
  13. Setup
  14. Case study: Sound Design Workflow
  15. Comparision
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Extended Backus-Naur Form of Mixer Script
  • Figure 2: The sound of "coin dropped to wooden table" descriped by Mixer Script.
  • Figure 3: The python code of "coin dropped to wooden table", using pre define library "AudioProcess" for signal processing.
  • Figure 4: Example of unified sound object. In the sound event list, each sound would be packaged to a object with it's name and metadata.
  • Figure 5: Behavior of SonicRAG in diffrent working situation. Left side shows how is RAG and Mixer enable LLM to synthsis audio. Central depicts adjustability of framework. Right part exhibits multimodel LLM can understand onomatopoeias and represent it to other media.