title: Apollo: Band-sequence Modeling for High-Quality Audio Restoration
summary: Audio restoration has become increasingly significant in modern society, not
  only due to the demand for high-quality auditory experiences enabled by
  advanced playback devices, but also because the growing capabilities of
  generative audio models necessitate high-fidelity audio. Typically, audio
  restoration is defined as a task of predicting undistorted audio from damaged
  input, often trained using a GAN framework to balance perception and
  distortion. Since audio degradation is primarily concentrated in mid- and
  high-frequency ranges, especially due to codecs, a key challenge lies in
  designing a generator capable of preserving low-frequency information while
  accurately reconstructing high-quality mid- and high-frequency content.
  Inspired by recent advancements in high-sample-rate music separation, speech
  enhancement, and audio codec models, we propose Apollo, a generative model
  designed for high-sample-rate audio restoration. Apollo employs an explicit
  frequency band split module to model the relationships between different
  frequency bands, allowing for more coherent and higher-quality restored audio.
  Evaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently
  outperforms existing SR-GAN models across various bit rates and music genres,
  particularly excelling in complex scenarios involving mixtures of multiple
  instruments and vocals. Apollo significantly improves music restoration quality
  while maintaining computational efficiency. The source code for Apollo is
  publicly available at https://github.com/JusperLee/Apollo.
paper_authors._id: 66e82fd4f9d6b6c5066a6e84
paper_authors.name: Kai Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-09-16T13:33:53.393Z
paper_authors._id: 6387676c23da90491eb9fb16
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1669818175965-noauth.jpeg
paper_authors.fullname: Kai Li
paper_authors.type: user
paper_authors.user: JusperLee
paper_authors._id: 66e82fd4f9d6b6c5066a6e85
paper_authors.name: Yi Luo
paper_title: Apollo: Band-sequence Modeling for High-Quality Audio Restoration
paper_summary: Audio restoration has become increasingly significant in modern society, not
  only due to the demand for high-quality auditory experiences enabled by
  advanced playback devices, but also because the growing capabilities of
  generative audio models necessitate high-fidelity audio. Typically, audio
  restoration is defined as a task of predicting undistorted audio from damaged
  input, often trained using a GAN framework to balance perception and
  distortion. Since audio degradation is primarily concentrated in mid- and
  high-frequency ranges, especially due to codecs, a key challenge lies in
  designing a generator capable of preserving low-frequency information while
  accurately reconstructing high-quality mid- and high-frequency content.
  Inspired by recent advancements in high-sample-rate music separation, speech
  enhancement, and audio codec models, we propose Apollo, a generative model
  designed for high-sample-rate audio restoration. Apollo employs an explicit
  frequency band split module to model the relationships between different
  frequency bands, allowing for more coherent and higher-quality restored audio.
  Evaluated on the MUSDB18-HQ and MoisesDB datasets, Apollo consistently
  outperforms existing SR-GAN models across various bit rates and music genres,
  particularly excelling in complex scenarios involving mixtures of multiple
  instruments and vocals. Apollo significantly improves music restoration quality
  while maintaining computational efficiency. The source code for Apollo is
  publicly available at https://github.com/JusperLee/Apollo.
paper_ai_keywords: GAN framework audio degradation mid-frequency ranges high-frequency ranges codecs low-frequency information generative model frequency band split module MUSDB18-HQ MoisesDB SR-GAN models