EVA-GAN: Enhanced Various Audio Generation via Scalable Generative
  Adversarial Networks
Shijia Liao Shiyi Lan Arun George Zachariah
The advent of Large Models marks a new era in machine learning, significantly
outperforming smaller models by leveraging vast datasets to capture and
synthesize complex patterns. Despite these advancements, the exploration into
scaling, especially in the audio generation domain, remains limited, with
previous efforts didn't extend into the high-fidelity (HiFi) 44.1kHz domain and
suffering from both spectral discontinuities and blurriness in the
high-frequency domain, alongside a lack of robustness against out-of-domain
data. These limitations restrict the applicability of models to diverse use
cases, including music and singing generation. Our work introduces Enhanced
Various Audio Generation via Scalable Generative Adversarial Networks
(EVA-GAN), yields significant improvements over previous state-of-the-art in
spectral and high-frequency reconstruction and robustness in out-of-domain data
performance, enabling the generation of HiFi audios by employing an extensive
dataset of 36,000 hours of 44.1kHz audio, a context-aware module, a
Human-In-The-Loop artifact measurement toolkit, and expands the model to
approximately 200 million parameters. Demonstrations of our work are available
at https://double-blind-eva-gan.cc.