title: EVA-GAN: Enhanced Various Audio Generation via Scalable Generative
    Adversarial Networks
summary: 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.
paper_authors._id: 65c049ba31e7709efbbbb85f
paper_authors.name: Shijia Liao
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-02-05T18:29:04.621Z
paper_authors._id: 63124e13b843665ddc69837a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63124e13b843665ddc69837a/Mx-yYSbBhoV3ueyOXFk0N.png
paper_authors.fullname: Lengyue
paper_authors.type: user
paper_authors.user: lengyue233
paper_authors._id: 65c049ba31e7709efbbbb860
paper_authors.name: Shiyi Lan
paper_authors._id: 65c049ba31e7709efbbbb861
paper_authors.name: Arun George Zachariah
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-05T12:21:02.215Z
paper_authors._id: 6599e082d3f2137415eefdeb
paper_authors.avatarUrl: /avatars/5b9088615caf47d520c48730cf182a63.svg
paper_authors.fullname: Arun George Zachariah
paper_authors.type: user
paper_authors.user: azachariah
paper_title: EVA-GAN: Enhanced Various Audio Generation via Scalable Generative
    Adversarial Networks
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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