Reproduce “AudioGenie: A Training-Free Multi-Agent Framework for Diverse Multimodality-to-Multiaudio Generation” on OpenResearch
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About this paper
Yan Rong, Jinting Wang, Guangzhi Lei, Shan Yang, Li Liu
AudioGenie presents a training-free multi-agent framework that orchestrates specialized models to generate diverse multi-type audio from complex multimodal inputs, establishing a new benchmark (MA-Bench) and outperforming existing state-of-the-art approaches in comprehensive audio synthesis.
Key insights
- Decomposing complex multimodal audio generation tasks into fine-grained sub-audio events and assigning them to domain-specific experts significantly enhances generation accuracy and diversity.
- A dual-layer agent architecture, where a supervisor team evaluates and guides a generation team, enables robust error detection, prevents error accumulation, and improves overall output quality and reliability.
- Leveraging pre-trained, specialized state-of-the-art models through an MLLM-driven orchestration framework can effectively overcome data scarcity and computational intensity challenges without requiring end-to-end system training.
Summary by alphaXiv.
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