AuDirector: A Self-Reflective Closed-Loop Framework for Immersive Audio Storytelling
Abstract
Despite advances in text and visual generation, creating coherent long-form audio narratives remains challenging. Existing frameworks often exhibit limitations such as mismatched character settings with voice performance, insufficient self-correction mechanisms, and limited human interactivity. To address these challenges, we propose AuDirector, a self-reflective closed-loop multi-agent framework. Specifically, it involves an Identity-Aware Pre-production mechanism that transforms narrative texts into character profiles and utterance-level emotional instructions to retrieve suitable voice candidates and guide expressive speech synthesis, thereby promoting context-aligned voice adaptation. To enhance quality, a Collaborative Synthesis and Correction module introduces a closed-loop self-correction mechanism to systematically audit and regenerate defective audio components. Furthermore, a Human-Guided Interactive Refinement module facilitates user control by interpreting natural language feedback to interactively refine the underlying scripts. Experiments demonstrate that AuDirector achieves superior performance compared to state-of-the-art baselines in structural coherence, emotional expressiveness, and acoustic fidelity. Audio samples can be found at https://anonymous-itsh.github.io/.
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
AuDirector presents a significant advancement in immersive audio storytelling through a self-reflective closed-loop multi-agent framework. Its innovative approach to integrating character profiling, emotional instruction, and user interaction sets a new standard in the field of audio generation, addressing key limitations of existing systems while demonstrating substantial technical impact and potential for broad applications.
Methodology
The methodology of AuDirector is innovative, integrating a multi-agent framework that combines identity-aware pre-production, collaborative synthesis and correction, and human-guided interactive refinement. Each component is well-defined, with a clear flow from narrative input to audio output. The use of a closed-loop self-correction mechanism is particularly noteworthy, as it addresses common pitfalls in generative audio systems, such as quality inconsistency and lack of user control. The framework's reliance on large language models (LLMs) for character profiling and emotional instruction generation is a strong point, allowing for nuanced audio generation that aligns closely with narrative context.
Results & evaluation
The experiments conducted are robust, comparing AuDirector against state-of-the-art baselines like WavJourney and PodAgent across both objective and subjective metrics. The use of diverse datasets, including podcasts and radio dramas, enhances the evaluation's credibility. The results indicate significant improvements in structural coherence, emotional expressiveness, and acoustic fidelity, validating the proposed framework's effectiveness. The ablation study further strengthens the findings by demonstrating the importance of the self-correction mechanism.
Reproducibility
The paper provides a comprehensive overview of the implementation details, including the specific agents used and the evaluation protocols. However, the lack of a publicly accessible code repository limits reproducibility. Future work could benefit from releasing the code and detailed instructions to facilitate further research and validation by the community.
Limitations
While AuDirector shows promise, it still faces challenges in generating non-speech audio tracks, particularly regarding acoustic diversity and nuance. This limitation could impact the overall immersion of the audio narratives. Additionally, the complexity of the system may pose challenges for users unfamiliar with audio production, potentially limiting its accessibility.
Broader impact
The potential applications of AuDirector are vast, ranging from enhancing audio storytelling in entertainment to educational tools that require immersive audio experiences. The framework could significantly impact industries such as gaming, film, and interactive media, where high-quality audio narratives are essential. Furthermore, the integration of user feedback into the generation process could lead to more personalized and engaging content. AuDirector presents a significant advancement in immersive audio storytelling through a self-reflective closed-loop multi-agent framework. Its innovative approach to integrating character profiling, emotional instruction, and user interaction sets a new standard in the field of audio generation, addressing key limitations of existing systems while demonstrating substantial technical impact and potential for broad applications.
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