Audio-Oscar: A Multi-Agent System for Complex Audio Scene Generation, Orchestration, and Refinement
Abstract
In recent years, audio generation has made significant progress in tasks such as text-to-speech (TTS), text-to-audio (TTA) and text-to-music (TTM). However, generating long-form and controllable audio from complex audio scene descriptions remains a significant challenge, as such scenes often require coordinated speech, sound effects, music, songs, temporal structure, and post-production. In this work, we introduce \textbf{Audio-Oscar}, a multi-agent framework for generating audio from complex descriptions. Audio-Oscar coordinates a set of specialist agents, each responsible for a different aspect of the audio scene, including character modeling and voice design, speech generation, fine-grained timeline planning, model selection, non-speech generation, and audio post-production. Audio-Oscar further incorporates feedback-driven refinement. In addition, to address the lack of suitable benchmarks for evaluating audio generation from complex audio scene descriptions, we construct \textbf{ASG-Bench}, an Audio Scene Generation Benchmark containing both scene descriptions paired with reference audio and text-only scene descriptions. Each scene is annotated with target audio events and temporal statements to evaluate whether the generated audio faithfully realizes the required scene content and temporal structure. Experimental results show that Audio-Oscar can effectively generate audio that matches complex scene descriptions. Project samples are available at https://audiooscar.github.io/. Our code is available at https://github.com/ziye26/Audio-Oscar.
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
Audio-Oscar represents a significant advancement in the field of audio generation, introducing a multi-agent framework that effectively orchestrates the creation of complex audio scenes from textual descriptions. The comprehensive methodology, innovative benchmark, and strong experimental results position this work as a valuable contribution to the machine learning and audio research communities.
Methodology
The methodology presented in Audio-Oscar is robust and innovative, employing a multi-agent framework that effectively decomposes the complex audio generation task into specialized roles, each addressing different aspects of audio scene creation. This structured approach allows for enhanced control over the generation process, including role modeling, timeline planning, and post-production refinement. The integration of feedback-driven refinement and the orchestration of various audio generation models are notable strengths, contributing to the overall effectiveness of the system.
Results & evaluation
The experiments conducted are comprehensive, utilizing a newly developed benchmark, ASG-Bench, which includes both reference audio and text-only descriptions. The results demonstrate that Audio-Oscar outperforms existing methods in generating complex audio scenes, achieving high scores in both objective metrics and subjective evaluations (MOS). The inclusion of human evaluations adds credibility to the findings, showcasing the system's ability to align generated audio with user expectations.
Reproducibility
The paper provides detailed implementation information, including the models used, evaluation protocols, and experimental setups. This level of detail enhances reproducibility, allowing other researchers to replicate the experiments and validate the results. The availability of the code and project samples further supports this aspect.
Limitations
While the system shows impressive capabilities, it does have limitations, particularly in generating realistic and emotionally expressive voices compared to real human recordings. Additionally, challenges remain in managing long audio requests and ensuring precise temporal alignment for all audio events. The reliance on large models for evaluation may also introduce variability in the assessment of generated audio quality.
Broader impact
The implications of this work are significant, as it addresses a critical gap in the field of audio generation by enabling the creation of complex audio scenes that can be utilized in various applications, including film, gaming, and virtual reality. The introduction of ASG-Bench as a benchmark can facilitate further research and development in this domain, promoting advancements in audio generation technologies. Audio-Oscar represents a significant advancement in the field of audio generation, introducing a multi-agent framework that effectively orchestrates the creation of complex audio scenes from textual descriptions. The comprehensive methodology, innovative benchmark, and strong experimental results position this work as a valuable contribution to the machine learning and audio research communities.
Author of this paper? Send a correction.