AudioSR: Versatile Audio Super-resolution at Scale
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.
6503f4e24d6120eff4f37483
Haohe Liu
admin_assigned
2023-09-15T09:15:53.239Z
6155245d1c762d4d61b51d5d
https://cdn-avatars.huggingface.co/v1/production/uploads/1677233708065-6155245d1c762d4d61b51d5d.png
haoheliu
user
haoheliu
6503f4e24d6120eff4f37484
Ke Chen
claimed_verified
2023-09-17T19:21:07.298Z
63fe457052441fe3e874ee22
https://cdn-avatars.huggingface.co/v1/production/uploads/1677608254704-noauth.jpeg
Ke Chen
user
RSNuts
6503f4e24d6120eff4f37485
Qiao Tian
6503f4e24d6120eff4f37486
Wenwu Wang
6503f4e24d6120eff4f37487
Mark D. Plumbley
AudioSR: Versatile Audio Super-resolution at Scale
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.
A diffusion-based generative model, AudioSR, achieves robust audio super-resolution across various audio types and bandwidths, enhancing generation quality for different audio models.
diffusion-based generative model audio super-resolution audio types sound effects music speech bandwidth range high-resolution audio sampling rate AudioLDM Fastspeech2 MusicGen