EVA-GAN: Enhanced Various Audio Generation via Scalable Generative
  Adversarial Networks
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.
65c049ba31e7709efbbbb85f
Shijia Liao
extracted_pending
2024-02-05T18:29:04.621Z
63124e13b843665ddc69837a
https://cdn-avatars.huggingface.co/v1/production/uploads/63124e13b843665ddc69837a/Mx-yYSbBhoV3ueyOXFk0N.png
Lengyue
user
lengyue233
65c049ba31e7709efbbbb860
Shiyi Lan
65c049ba31e7709efbbbb861
Arun George Zachariah
admin_assigned
2024-02-05T12:21:02.215Z
6599e082d3f2137415eefdeb
/avatars/5b9088615caf47d520c48730cf182a63.svg
Arun George Zachariah
user
azachariah
EVA-GAN: Enhanced Various Audio Generation via Scalable Generative
  Adversarial Networks
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.
EVA-GAN improves HiFi audio generation by using a large dataset and scalable GANs with a context-aware module, enhancing spectral and high-frequency accuracy and out-of-domain robustness.
Large Models Generative Adversarial Networks (GANs) high-fidelity (HiFi) 44.1kHz spectral discontinuities blurriness out-of-domain data context-aware module Human-In-The-Loop artifact measurement toolkit