title: AudioSR: Versatile Audio Super-resolution at Scale
summary: Audio super-resolution is a fundamental task that predicts high-frequency
  components for low-resolution audio, enhancing audio quality in digital
  applications. Previous methods have limitations such as the limited scope of
  audio types (e.g., music, speech) and specific bandwidth settings they can
  handle (e.g., 4kHz to 8kHz). In this paper, we introduce a diffusion-based
  generative model, AudioSR, that is capable of performing robust audio
  super-resolution on versatile audio types, including sound effects, music, and
  speech. Specifically, AudioSR can upsample any input audio signal within the
  bandwidth range of 2kHz to 16kHz to a high-resolution audio signal at 24kHz
  bandwidth with a sampling rate of 48kHz. Extensive objective evaluation on
  various audio super-resolution benchmarks demonstrates the strong result
  achieved by the proposed model. In addition, our subjective evaluation shows
  that AudioSR can acts as a plug-and-play module to enhance the generation
  quality of a wide range of audio generative models, including AudioLDM,
  Fastspeech2, and MusicGen. Our code and demo are available at
  https://audioldm.github.io/audiosr.
paper_authors._id: 6503f4e24d6120eff4f37483
paper_authors.name: Haohe Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-09-15T09:15:53.239Z
paper_authors._id: 6155245d1c762d4d61b51d5d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677233708065-6155245d1c762d4d61b51d5d.png
paper_authors.fullname: haoheliu
paper_authors.type: user
paper_authors.user: haoheliu
paper_authors._id: 6503f4e24d6120eff4f37484
paper_authors.name: Ke Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-17T19:21:07.298Z
paper_authors._id: 63fe457052441fe3e874ee22
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677608254704-noauth.jpeg
paper_authors.fullname: Ke Chen
paper_authors.type: user
paper_authors.user: RSNuts
paper_authors._id: 6503f4e24d6120eff4f37485
paper_authors.name: Qiao Tian
paper_authors._id: 6503f4e24d6120eff4f37486
paper_authors.name: Wenwu Wang
paper_authors._id: 6503f4e24d6120eff4f37487
paper_authors.name: Mark D. Plumbley
paper_title: AudioSR: Versatile Audio Super-resolution at Scale
paper_summary: Audio super-resolution is a fundamental task that predicts high-frequency
  components for low-resolution audio, enhancing audio quality in digital
  applications. Previous methods have limitations such as the limited scope of
  audio types (e.g., music, speech) and specific bandwidth settings they can
  handle (e.g., 4kHz to 8kHz). In this paper, we introduce a diffusion-based
  generative model, AudioSR, that is capable of performing robust audio
  super-resolution on versatile audio types, including sound effects, music, and
  speech. Specifically, AudioSR can upsample any input audio signal within the
  bandwidth range of 2kHz to 16kHz to a high-resolution audio signal at 24kHz
  bandwidth with a sampling rate of 48kHz. Extensive objective evaluation on
  various audio super-resolution benchmarks demonstrates the strong result
  achieved by the proposed model. In addition, our subjective evaluation shows
  that AudioSR can acts as a plug-and-play module to enhance the generation
  quality of a wide range of audio generative models, including AudioLDM,
  Fastspeech2, and MusicGen. Our code and demo are available at
  https://audioldm.github.io/audiosr.
paper_ai_summary: A diffusion-based generative model, AudioSR, achieves robust audio super-resolution across various audio types and bandwidths, enhancing generation quality for different audio models.
paper_ai_keywords: diffusion-based generative model audio super-resolution audio types sound effects music speech bandwidth range high-resolution audio sampling rate AudioLDM Fastspeech2 MusicGen