Audio Super-Resolution

Audio super-resolution, especially speech, refers to the process of reconstructing high-resolution music signals from their low-resolution counterparts. Essentially, it enhances the quality of a speech signal by increasing its sampling rate or bandwidth while preserving naturalness and intelligibility. A representative Github project for speech super-resolution is ClearerVoice-Studio.

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 11–20 of 22 papers

Title	Date	Tasks	Status	Hype
Neural Vocoder is All You Need for Speech Super-resolution	Mar 28, 2022	AllAudio Super-Resolution	CodeCode Available	1
Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution	Oct 30, 2021	Audio Super-ResolutionSelf-Supervised Learning	CodeCode Available	1
TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining	Oct 26, 2021	Audio Super-ResolutionBandwidth Extension	CodeCode Available	1
An investigation of pre-upsampling generative modelling and Generative Adversarial Networks in audio super resolution	Sep 30, 2021	Audio GenerationAudio Super-Resolution	—Unverified	0
Self-Attention for Audio Super-Resolution	Aug 26, 2021	Audio Super-ResolutionSuper-Resolution	CodeCode Available	1
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling	Apr 6, 2021	Audio Super-ResolutionSuper-Resolution	CodeCode Available	1
On Filter Generalization for Music Bandwidth Extension Using Deep Neural Networks	Nov 14, 2020	Audio Super-ResolutionBandwidth Extension	CodeCode Available	1
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations.	Dec 1, 2019	Audio Super-ResolutionSuper-Resolution	CodeCode Available	0
Learning to Have an Ear for Face Super-Resolution	Sep 27, 2019	Audio Super-ResolutionFace Reconstruction	—Unverified	0
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations	Sep 14, 2019	Audio Super-ResolutionSuper-Resolution	CodeCode Available	0

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All datasets VCTK Multi-Speaker Piano Voice Bank corpus (VCTK)DSD100

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	U-Net	Log-Spectral Distance	3.1	—	Unverified
2	U-Net + TFiLM	Log-Spectral Distance	1.8	—	Unverified
3	U-Net + AFiLM	Log-Spectral Distance	1.7	—	Unverified
4	TUNet	Log-Spectral Distance	1.36	—	Unverified
5	TUNet + MSM pre-training	Log-Spectral Distance	1.28	—	Unverified
6	NVSR	Log-Spectral Distance	0.78	—	Unverified
7	CMGAN	Log-Spectral Distance	0.76	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	U-Net	Log-Spectral Distance	3.4	—	Unverified
2	U-Net + TFiLM	Log-Spectral Distance	2	—	Unverified
3	U-Net + AFiLM	Log-Spectral Distance	1.5	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	U-Net	Log-Spectral Distance	3.2	—	Unverified
2	U-Net + TFiLM	Log-Spectral Distance	2.5	—	Unverified
3	U-Net + AFiLM	Log-Spectral Distance	2.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	U-Net and ResNet	SNR	35.26	—	Unverified