AudioMoG: Guiding Audio Generation with Mixture-of-Guidance
Abstract
Guidance methods have demonstrated significant improvements in cross-modal audio generation, including text-to-audio (T2A) and video-to-audio (V2A) generation. The popularly adopted method, classifier-free guidance (CFG), steers generation by emphasizing condition alignment, enhancing fidelity but often at the cost of diversity. Recently, autoguidance (AG) has been explored for audio generation, encouraging the sampling to faithfully reconstruct the target distribution and showing increased diversity. Despite these advances, they usually rely on a single guiding principle, e.g., condition alignment in CFG or score accuracy in AG, leaving the full potential of guidance for audio generation untapped. In this work, we explore enriching the composition of the guidance method and present a mixture-of-guidance framework, AudioMoG. Within the design space, AudioMoG can exploit the complementary advantages of distinctive guiding principles by fulfilling their cumulative benefits. With a reduced form, AudioMoG can consider parallel complements or recover a single guiding principle, without sacrificing generality. We experimentally show that, given the same inference speed, AudioMoG approach consistently outperforms single guidance in T2A generation across sampling steps, concurrently showing advantages in V2A, text-to-music, and image generation. These results highlight a "free lunch" in current cross-modal audio generation systems: higher quality can be achieved through mixed guiding principles at the sampling stage without sacrificing inference efficiency. Demo samples are available at: https://audio-mog.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
The main contribution of this paper is the introduction of AudioMoG, a novel mixture-of-guidance framework for audio generation that significantly improves synthesis quality while maintaining inference efficiency across multiple tasks. The comprehensive evaluation of the methodology and results demonstrates its potential to advance the state of the art in audio generation.
Methodology
The paper introduces a novel mixture-of-guidance framework, AudioMoG, which combines multiple guidance principles (classifier-free guidance and autoguidance) to improve audio generation quality without sacrificing inference efficiency. The methodology is well-structured, with a clear explanation of how the framework operates and the theoretical underpinnings of the guidance methods. The authors provide a comprehensive analysis of the limitations of existing methods, which strengthens the rationale for their approach. The framework's flexibility to switch between different guiding principles and its empirical validation across various tasks enhance its methodological robustness.
Results & evaluation
The experimental section is thorough, utilizing multiple datasets (AudioSet, FSD50k, Clotho v2, etc.) and evaluation metrics (FAD, KL divergence, IS, etc.) to assess the performance of AudioMoG against baseline methods. The results consistently demonstrate superior performance in T2A, V2A, and text-to-music generation tasks, indicating the effectiveness of the proposed method. The use of both objective and subjective evaluations adds depth to the findings, although the subjective evaluation could benefit from a larger sample size for more robust conclusions.
Reproducibility
The paper provides detailed descriptions of the model configurations, training processes, and evaluation metrics, which are essential for reproducibility. However, the absence of a publicly available code repository limits the ease with which other researchers can replicate the results. Including the code would significantly enhance the reproducibility of the findings.
Limitations
While the proposed framework shows promising results, the paper does not extensively address potential limitations, such as the complexity introduced by combining multiple guidance methods and the computational overhead that may arise in practice. Additionally, the sensitivity of the framework to the choice of guidance scales and the need for careful tuning could be highlighted more prominently.
Broader impact
The advancements in audio generation techniques presented in this paper have significant implications for various applications, including content creation, gaming, and virtual reality. By improving the fidelity and diversity of generated audio, the proposed methods could enhance user experiences in multimedia applications. Furthermore, the framework's adaptability to different modalities (text, video, and music) suggests potential cross-domain applications that could benefit from improved audio generation techniques. The main contribution of this paper is the introduction of AudioMoG, a novel mixture-of-guidance framework for audio generation that significantly improves synthesis quality while maintaining inference efficiency across multiple tasks. The comprehensive evaluation of the methodology and results demonstrates its potential to advance the state of the art in audio generation.
Author of this paper? Send a correction.