# Audio Super Resolution using Neural Networks

arXiv: https://arxiv.org/abs/1708.00853 · HTML: https://mlsanity.com/paper/1708.00853/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 26 of 26)

- [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://mlsanity.com/paper/2301.12503/) (2023)
- [A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AI](https://mlsanity.com/paper/2303.13336/) (2023)
- [NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling](https://mlsanity.com/paper/2104.02321/) (2021)
- [HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement](https://mlsanity.com/paper/2203.13086/) (2022)
- [Neural Vocoder is All You Need for Speech Super-resolution](https://mlsanity.com/paper/2203.14941/) (2022)
- [NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates](https://mlsanity.com/paper/2206.08545/) (2022)
- [Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations](https://mlsanity.com/paper/2303.01664/) (2023)
- [AERO: Audio Super Resolution in the Spectral Domain](https://mlsanity.com/paper/2211.12232/) (2022)
- [Audio Super-Resolution with Latent Bridge Models](https://mlsanity.com/paper/2509.17609/) (2025)
- [Symbolic music generation conditioned on continuous-valued emotions](https://mlsanity.com/paper/2203.16165/) (2022)
- [Learning Continuous Representation of Audio for Arbitrary Scale Super Resolution](https://mlsanity.com/paper/2111.00195/) (2021)
- [Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolution](https://mlsanity.com/paper/2409.09337/) (2024)
- [TUNet: A Block-online Bandwidth Extension Model based on Transformers and Self-supervised Pretraining](https://mlsanity.com/paper/2110.13492/) (2021)
- [UnDiff: Unsupervised Voice Restoration with Unconditional Diffusion Model](https://mlsanity.com/paper/2306.00721/) (2023)
- [mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra](https://mlsanity.com/paper/2305.11104/) (2023)
- [Sound and Visual Representation Learning with Multiple Pretraining Tasks](https://mlsanity.com/paper/2201.01046/) (2022)
- [SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices](https://mlsanity.com/paper/2101.07996/) (2021)
- [UniverSR: Unified and Versatile Audio Super-Resolution via Vocoder-Free Flow Matching](https://mlsanity.com/paper/2510.00771/) (2025)
- [STAA-Net: A Sparse and Transferable Adversarial Attack for Speech Emotion Recognition](https://mlsanity.com/paper/2402.01227/) (2024)
- [DCAE-SR: Design of a Denoising Convolutional Autoencoder for reconstructing Electrocardiograms signals at Super Resolution](https://mlsanity.com/paper/2404.15307/) (2024)
- [LatentFlowSR: High-Fidelity Audio Super-Resolution via Noise-Robust Latent Flow Matching](https://mlsanity.com/paper/2604.09188/) (2026)
- [Audio Spectral Enhancement: Leveraging Autoencoders for Low Latency Reconstruction of Long, Lossy Audio Sequences](https://mlsanity.com/paper/2108.03703/) (2021)
- [Speech Bandwidth Expansion Via High Fidelity Generative Adversarial Networks](https://mlsanity.com/paper/2407.18571/) (2024)
- [SUBARU: A Practical Approach to Power Saving in Hearables Using SUB-Nyquist Audio Resolution Upsampling](https://mlsanity.com/paper/2506.22321/) (2025)
- [Discriminating real and synthetic super-resolved audio samples using embedding-based classifiers](https://mlsanity.com/paper/2601.03443/) (2026)
- [On the Geometry of Music Bandwidth Extension in Latent Spaces of Audio Codecs](https://mlsanity.com/paper/2608.03721/) (2026)

## Abstract (arXiv)

We introduce a new audio processing technique that increases the sampling rate of signals such as speech or music using deep convolutional neural networks. Our model is trained on pairs of low and high-quality audio examples; at test-time, it predicts missing samples within a low-resolution signal in an interpolation process similar to image super-resolution. Our method is simple and does not involve specialized audio processing techniques; in our experiments, it outperforms baselines on standard speech and music benchmarks at upscaling ratios of 2x, 4x, and 6x. The method has practical applications in telephony, compression, and text-to-speech generation; it demonstrates the effectiveness of feed-forward convolutional architectures on an audio generation task.