Apollo: Band-sequence Modeling for High-Quality Audio Restoration
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.
66e82fd4f9d6b6c5066a6e84
Kai Li
extracted_confirmed
2024-09-16T13:33:53.393Z
6387676c23da90491eb9fb16
https://cdn-avatars.huggingface.co/v1/production/uploads/1669818175965-noauth.jpeg
Kai Li
user
JusperLee
66e82fd4f9d6b6c5066a6e85
Yi Luo
Apollo: Band-sequence Modeling for High-Quality Audio Restoration
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.
Apollo, a generative model for high-sample-rate audio restoration, uses a frequency band split module to enhance restored audio quality while maintaining efficiency, outperforming existing SR-GAN models on MUSDB18-HQ and MoisesDB datasets.
GAN frequency band split module high-sample-rate audio restoration MUSDB18-HQ MoisesDB SR-GAN