Browse State-of-the-Art › Audio Super-Resolution
Audio Super-Resolution
16 papers with code · 4 benchmarks · 3 datasets archive 2025-07-28
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.
Description from the archive archive 2025-07-28.
Benchmarks archive 2025-07-28
4 leaderboard tables shown for this task, 4 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.
| Dataset | Best model (first row in archive order) | Paper | Code | Syntology | Compare |
|---|---|---|---|---|---|
| VCTK Multi-Speaker (7 rows) | CMGAN | CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement | code | — | Compare |
| Piano (3 rows) | U-Net + AFiLM | Self-Attention for Audio Super-Resolution | code | — | Compare |
| Voice Bank corpus (VCTK) (3 rows) | U-Net + AFiLM | Self-Attention for Audio Super-Resolution | code | — | Compare |
| DSD100 (1 row) | U-Net and ResNet | On Filter Generalization for Music Bandwidth Extension Using Deep... | code | — | Compare |
Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Parent tasks archive 2025-07-28
Most implemented papers archive 2025-07-28
16 shown of 16 papers with code (22 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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17 Jun 2022 5 repositories listed Syntology ran 13 of 18 samples · 5 unverifiedConventionally, audio super-resolution models fixed the initial and the target sampling rates, which necessitate the model to be trained for each pair of sampling rates.
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2 Aug 2017 4 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks.
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6 Apr 2021 3 repositories listed Syntology ran 2 of 2 samples · 0 unverifiedIn this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz.
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22 Sep 2022 2 repositories listedRather than focusing exclusively on the speech denoising task, we extend this work to address the dereverberation and super-resolution tasks.
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14 Nov 2020 2 repositories listedIn this paper, we address a sub-topic of the broad domain of audio enhancement, namely musical audio bandwidth extension.
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9 Jan 2025 1 repository listed Syntology ran 10 of 15 samples · 5 unverifiedAudio super-resolution is challenging owing to its ill-posed nature.
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11 Nov 2024 1 repository listedWith the proposed AEROMamba, training requires 2-4x less GPU memory, since Mamba exploits the convolutional formulation and leverages GPU memory hierarchy.
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13 Sep 2023 1 repository listed Syntology ran 1 of 2 samples · 1 unverifiedAudio super-resolution is a fundamental task that predicts high-frequency components for low-resolution audio, enhancing audio quality in digital applications.
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22 Nov 2022 1 repository listed Syntology ran 0 of 8 samples · 8 unverifiedWe optimize the model using both time and frequency domain loss functions.
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28 Oct 2022 1 repository listedNeural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs.
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28 Mar 2022 1 repository listedIn this paper, we propose a neural vocoder based speech super-resolution method (NVSR) that can handle a variety of input resolution and upsampling ratios.
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30 Oct 2021 1 repository listed Syntology ran 3 of 7 samples · 4 unverifiedTo obtain a continuous representation of audio and enable super resolution for arbitrary scale factor, we propose a method of implicit neural representation, coined Local Implicit representation for Super resolution of…
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26 Oct 2021 1 repository listedWe introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension.
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26 Aug 2021 1 repository listedConvolutions operate only locally, thus failing to model global interactions.
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1 Dec 2019 1 repository listedLearning representations that accurately capture long-range dependencies in sequential inputs --- including text, audio, and genomic data --- is a key problem in deep learning.
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14 Sep 2019 1 repository listedLearning representations that accurately capture long-range dependencies in sequential inputs -- including text, audio, and genomic data -- is a key problem in deep learning.
Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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