Enhancing GAN-Based Neural Vocoders with Slicing Adversarial Network

Core Concepts

SAN can effectively improve GAN-based vocoders, including BigVGAN, through small modifications and soft monotonization.

Abstract

Introduction to Speech Synthesis: Two-stage pipeline for speech synthesis. Focus on vocoders synthesizing waveforms from mel-spectrograms. Improving Vocoder Quality: Various deep generative models enhance waveform synthesis. GANs like BigVGAN excel in high-fidelity waveform generation. Challenges in Discrimination: Most GANs struggle with optimal feature projection for discrimination. Introduction of slicing adversarial network (SAN) for improved training framework. Applying SAN to Vocoder Task: Investigating SAN effectiveness in audio waveform generation. Challenges in applying SAN to least-squares GAN due to non-monotonicity. Proposed Modification Scheme: Soft monotonization to convert least-squares GAN to least-squares SAN. Experimental Results: Application of SAN framework (BigVSAN) outperforms BigVGAN in various evaluations. Further Experiments: Extension of SAN to moderate-sized neural vocoders like MelGAN and Parallel WaveGAN showing improved performance across datasets.

Stats

"BigVSAN outperforms BigVGAN in all objective and subjective evaluations." "We train all the above models for 1M steps." "Objective evaluations conducted on LibriTTS dev-clean and dev-other subsets."

Quotes

"SAN can improve the performance of various vocoders, including BigVGAN." "Soft monotonization scheme proposed to modify least-squares GAN to least-squares SAN."

Key Insights Distilled From

BigVSAN

by Takashi Shib... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2309.02836.pdf

Deeper Inquiries

How does the introduction of SAN impact the training stability compared to traditional GANs

The introduction of SAN (Slicing Adversarial Network) impacts training stability by enhancing the discriminators to find more informative linear projections for discriminating between real and fake samples. Traditional GANs often struggle to obtain the most discriminative projection in the feature space, leading to suboptimal results. SAN addresses this issue by focusing on learning the last projection layer that can best distinguish real and fake samples in a given feature space. By ensuring that the derivative of the function representing this discrimination is monotonically decreasing, SAN improves training stability compared to traditional GANs. This enhancement allows for better convergence during training and can lead to improved performance in tasks like image generation or vocoding.

What are the implications of using snakebeta activation over snake activation in terms of objective metrics and human perception

When comparing snakebeta activation with snake activation in neural vocoders, there are notable implications both in terms of objective metrics and human perception. Snakebeta activation tends to generate artifacts in high-frequency bands, which can negatively impact metrics like MCD (Mel-Cepstral Distortion) and M-STFT (Multi-Resolution STFT). These metrics consider differences across frequency ranges where artifacts may be more pronounced with snakebeta activation. However, human perception assessments such as MOS (Mean Opinion Score) might not be as affected by these artifacts since they focus on overall quality rather than specific spectral distortions. In some cases, raters may prefer snakebeta due to its perceived quality despite negative effects on certain objective metrics.

How might the findings from this study influence future research directions in neural vocoder development

The findings from this study could influence future research directions in neural vocoder development by highlighting the effectiveness of incorporating SAN into existing models for improved performance. Researchers may explore further applications of SAN across different types of generative models beyond vocoders, leveraging its ability to enhance discriminator networks for better sample discrimination. Additionally, investigating alternative activations or modifications like soft monotonization could become a focal point for optimizing neural vocoders based on specific evaluation criteria such as FAD (Fréchet Audio Distance). Future studies might also delve into understanding how different hyperparameters or architectural choices interact with SAN implementation to achieve optimal results in various speech synthesis tasks.