AudioGenie: A Training-Free Multi-Agent Framework for Diverse Multimodality-to-Multiaudio Generation
AudioGenie: A training-free multi-agent framework for multimodality-to-multiaudio generation, achieving SOTA performance.
Key Findings
Methodology
AudioGenie employs a training-free multi-agent system with a dual-layer architecture consisting of a generation team and a supervisor team. The generation team handles task decomposition and expert model selection, while the supervisor team ensures temporal-spatial consistency and feedback verification.
Key Results
- AudioGenie achieves state-of-the-art or comparable performance across 9 metrics in 8 tasks, validated by user studies on quality, accuracy, alignment, and aesthetics.
- In the MA-Bench benchmark, AudioGenie excels in multi-type audio generation tasks.
- The trial-and-error iterative refinement module allows dynamic evaluation and correction of outputs, enhancing reliability.
Significance
This research provides an innovative solution for multimodality-to-multiaudio generation, addressing data scarcity and the lack of robust multi-task learning frameworks, advancing human-computer interaction.
Technical Contribution
Introduces a training-free multi-agent system combining task decomposition, expert model selection, and trial-and-error iterative refinement, significantly enhancing multimodal understanding and audio generation flexibility and accuracy.
Novelty
AudioGenie is the first to achieve multimodality-to-multiaudio generation without training, using a multi-agent system to overcome the limitations of single models handling diverse audio events.
Limitations
- The system may experience performance degradation when handling extremely complex audio events, especially with unclear inputs.
- Due to the training-free mechanism, adaptability to new types of audio events is limited.
Future Work
Future work could explore mechanisms for handling more complex audio events and expand the system to support more audio types and input modalities.
AI Executive Summary
Multimodality-to-multiaudio generation faces challenges in synthesizing diverse and contextually aligned audio types from multimodal inputs due to the scarcity of high-quality paired datasets and robust multi-task learning frameworks.
AudioGenie proposes a training-free multi-agent system with a dual-layer architecture consisting of a generation team and a supervisor team. The generation team handles task decomposition and expert model selection, while the supervisor team ensures temporal-spatial consistency and feedback verification.
Experiments show that AudioGenie achieves state-of-the-art performance across multiple tasks and metrics, validated by user studies on quality, accuracy, alignment, and aesthetics. This system opens new possibilities for multimodal human-computer interaction.
However, the system may experience performance degradation when handling extremely complex audio events, especially with unclear inputs. Future work could explore mechanisms for handling more complex audio events and expand the system to support more audio types and input modalities.
Deep Analysis
Background
Multimodality-to-multiaudio generation aims to produce various audio types from multimodal inputs like video, text, and images. Recent advances in text-to-audio and video-to-audio generation have been significant, but the lack of large-scale paired datasets limits the development of a unified framework.
Core Problem
Multimodality-to-multiaudio generation faces three main challenges: inadequate fine-grained understanding of multimodal inputs, inability of single models to handle diverse audio events, and absence of self-correction mechanisms for reliable outputs.
Innovation
AudioGenie addresses these challenges with a training-free multi-agent system. The generation team uses task decomposition and adaptive expert model selection, while the supervisor team ensures output consistency through feedback loops.
Methodology
- �� The generation team decomposes complex audio requirements into manageable sub-events.
- �� Adaptive expert model selection chooses suitable generation models.
- �� Trial-and-error iterative refinement module uses feedback loops for self-correction and output optimization.
Experiments
Experiments use the MA-Bench benchmark, comprising 198 annotated videos with multi-type audios. AudioGenie is evaluated across 9 metrics in 8 tasks, with user studies validating its effectiveness in quality, accuracy, alignment, and aesthetics.
Results
AudioGenie achieves state-of-the-art performance across multiple tasks and metrics, particularly excelling in multi-type audio generation tasks. The trial-and-error iterative module enhances output reliability.
Applications
The system can be applied in game development, film production, and VR/AR experiences, advancing the field of multimodal human-computer interaction.
Limitations & Outlook
The system may experience performance degradation when handling extremely complex audio events, especially with unclear inputs. Future work could explore mechanisms for handling more complex audio events.
Plain Language Accessible to non-experts
Imagine a concert where different bands play various styles of music. AudioGenie acts like a super conductor, selecting the right band and adjusting their performance based on audience needs and the scene. Even if a band makes a mistake, the conductor can quickly correct and optimize the show.
ELI14 Explained like you're 14
Imagine playing a game with various sounds like character dialogues, background music, and sound effects. AudioGenie is like a smart game designer, automatically choosing and generating the best sounds for the game scenes, making the experience more realistic and fun! If a sound isn't quite right, it can adjust itself to ensure every sound perfectly matches the game's storyline.
Glossary
Multimodality
Involves multiple input forms like video, text, and images.
Used in the paper to describe the diversity of input data.
Multiaudio Generation
The process of generating various audio types from multimodal inputs.
The main research goal of the paper.
Multi-Agent System
A system composed of multiple independent agents, each responsible for specific tasks.
Used for collaborative task execution.
Self-Correction
The system's ability to automatically correct output errors through feedback loops.
A crucial mechanism for improving output quality and reliability.
Training-Free
System design that achieves functionality without prior training.
One of AudioGenie's core innovations.
Open Questions Unanswered questions from this research
- 1 How to handle more complex audio events without training?
- 2 How to improve adaptability to new types of audio events?
Applications
Immediate Applications
Game Development
AudioGenie can be used to generate background music and sound effects in games, enhancing player experience.
Film Production
By generating diverse audio, AudioGenie can provide richer sound effects for movie scenes.
Long-term Vision
Virtual Reality Experience
AudioGenie can enhance the realism of sounds in virtual reality environments, advancing VR technology.
Abstract
Multimodality-to-Multiaudio (MM2MA) generation faces significant challenges in synthesizing diverse and contextually aligned audio types (e.g., sound effects, speech, music, and songs) from multimodal inputs (e.g., video, text, images), owing to the scarcity of high-quality paired datasets and the lack of robust multi-task learning frameworks. Recently, multi-agent system shows great potential in tackling the above issues. However, directly applying it to MM2MA task presents three critical challenges: (1) inadequate fine-grained understanding of multimodal inputs (especially for video), (2) the inability of single models to handle diverse audio events, and (3) the absence of self-correction mechanisms for reliable outputs. To this end, we propose AudioGenie, a novel training-free multi-agent system featuring a dual-layer architecture with a generation team and a supervisor team. For the generation team, a fine-grained task decomposition and an adaptive Mixture-of-Experts (MoE) collaborative entity are designed for detailed comprehensive multimodal understanding and dynamic model selection, and a trial-and-error iterative refinement module is designed for self-correction. The supervisor team ensures temporal-spatial consistency and verifies outputs through feedback loops. Moreover, we build MA-Bench, the first benchmark for MM2MA tasks, comprising 198 annotated videos with multi-type audios. Experiments demonstrate that our AudioGenie achieves state-of-the-art (SOTA) or comparable performance across 9 metrics in 8 tasks. User study further validates the effectiveness of our method in terms of quality, accuracy, alignment, and aesthetic. The project website with audio samples can be found at https://audiogenie.github.io/.