BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation

• URL: http://arxiv.org/abs/2506.09487v1
• Date: Wed, 11 Jun 2025 07:57:05 GMT
• Title: BemaGANv2: A Tutorial and Comparative Survey of GAN-based Vocoders for Long-Term Audio Generation
• Authors: Taesoo Park, Mungwi Jeong, Mingyu Park, Narae Kim, Junyoung Kim, Mujung Kim, Jisang Yoo, Hoyun Lee, Sanghoon Kim, Soonchul Kwon,
• Abstract summary: This paper presents a tutorial-style survey and implementation guide of BemaGANv2.<n>BemaGANv2 is an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation.
• Score: 5.716013795091872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a tutorial-style survey and implementation guide of BemaGANv2, an advanced GAN-based vocoder designed for high-fidelity and long-term audio generation. Built upon the original BemaGAN architecture, BemaGANv2 incorporates major architectural innovations by replacing traditional ResBlocks in the generator with the Anti-aliased Multi-Periodicity composition (AMP) module, which internally applies the Snake activation function to better model periodic structures. In the discriminator framework, we integrate the Multi-Envelope Discriminator (MED), a novel architecture we originally proposed, to extract rich temporal envelope features crucial for periodicity detection. Coupled with the Multi-Resolution Discriminator (MRD), this combination enables more accurate modeling of long-range dependencies in audio. We systematically evaluate various discriminator configurations, including MSD + MED, MSD + MRD, and MPD + MED + MRD, using objective metrics (FAD, SSIM, PLCC, MCD) and subjective evaluations (MOS, SMOS). This paper also provides a comprehensive tutorial on the model architecture, training methodology, and implementation to promote reproducibility. The code and pre-trained models are available at: https://github.com/dinhoitt/BemaGANv2.

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