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arXiv · 2412.08577

Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation

Hongming Guo·Ruibo Fu·Yizhong Geng·Shuai Liu·Shuchen Shi·Tao Wang·Chunyu Qiang·Chenxing Li·Ya Li·Zhengqi Wen·Yukun Liu·Xuefei Liu
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Abstract

Text-to-audio (TTA) model is capable of generating diverse audio from textual prompts. However, most mainstream TTA models, which predominantly rely on Mel-spectrograms, still face challenges in producing audio with rich content. The intricate details and texture required in Mel-spectrograms for such audio often surpass the models' capacity, leading to outputs that are blurred or lack coherence. In this paper, we begin by investigating the critical role of U-Net in Mel-spectrogram generation. Our analysis shows that in U-Net structure, high-frequency components in skip-connections and the backbone influence texture and detail, while low-frequency components in the backbone are critical for the diffusion denoising process. We further propose ``Mel-Refine'', a plug-and-play approach that enhances Mel-spectrogram texture and detail by adjusting different component weights during inference. Our method requires no additional training or fine-tuning and is fully compatible with any diffusion-based TTA architecture. Experimental results show that our approach boosts performance metrics of the latest TTA model Tango2 by 25\%, demonstrating its effectiveness.

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BibTeXRIS

Hongming Guo, Ruibo Fu, Yizhong Geng, Shuai Liu, Shuchen Shi, Tao Wang, Chunyu Qiang, Chenxing Li, Ya Li, Zhengqi Wen, Yukun Liu, Xuefei Liu. 2024-12-11. Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation. https://arxiv.org/abs/2412.08577

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