mlsanityData to 7 Oct 2026

Upsampling artifacts in neural audio synthesis

arXiv 2010.14356 ↗First posted 27 Oct 2020cs.SD cs.LG eess.AScited by 28

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Abstract (arXiv)

A number of recent advances in neural audio synthesis rely on upsampling layers, which can introduce undesired artifacts. In computer vision, upsampling artifacts have been studied and are known as checkerboard artifacts (due to their characteristic visual pattern). However, their effect has been overlooked so far in audio processing. Here, we address this gap by studying this problem from the audio signal processing perspective. We first show that the main sources of upsampling artifacts are: (i) the tonal and filtering artifacts introduced by problematic upsampling operators, and (ii) the spectral replicas that emerge while upsampling. We then compare different upsampling layers, showing that nearest neighbor upsamplers can be an alternative to the problematic (but state-of-the-art) transposed and subpixel convolutions which are prone to introduce tonal artifacts.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Oct 2020.

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Citing papers per year: 2021: 5; 2022: 10; 2023: 3; 2024: 3; 2025: 3; 2026: 4.

28 papers.

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  1. 2023High-Fidelity Audio Compression with Improved RVQGANcited by 423
  2. 2022BigVGAN: A Universal Neural Vocoder with Large-Scale Trainingcited by 245
  3. 2021Hybrid Spectrogram and Waveform Source Separationcited by 90
  4. 2022Universal Speech Enhancement with Score-based Diffusioncited by 75
  5. 2023A Survey on Audio Diffusion Models: Text To Speech Synthesis and Enhancement in Generative AIcited by 68
  6. 2024The Codecfake Dataset and Countermeasures for the Universally Detection of Deepfake Audiocited by 31
  7. 2021Fre-GAN: Adversarial Frequency-consistent Audio Synthesiscited by 24
  8. 2022Avocodo: Generative Adversarial Network for Artifact-free Vocodercited by 18
  9. 2022On loss functions and evaluation metrics for music source separationcited by 17
  10. 2021GAN Vocoder: Multi-Resolution Discriminator Is All You Needcited by 16
  11. 2021Self-Supervised VQ-VAE for One-Shot Music Style Transfercited by 13
  12. 2022Automatic music mixing with deep learning and out-of-domain datacited by 11
16 more
  1. 2024Wave-U-Mamba: An End-To-End Framework For High-Quality And Efficient Speech Super Resolutioncited by 9
  2. 2022Music Separation Enhancement with Generative Modelingcited by 8
  3. 2022Adversarial Permutation Invariant Training for Universal Sound Separationcited by 8
  4. 2022DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networkscited by 4
  5. 2026Woosh: A Sound Effects Foundation Modelcited by 4
  6. 2023Mono-to-stereo through parametric stereo generationcited by 3
  7. 2021Upsampling layers for music source separationcited by 2
  8. 2022EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Acceleratorscited by 1
  9. 2025LM-SPT: LM-Aligned Semantic Distillation for Speech Tokenizationcited by 1
  10. 2025Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?cited by 1
  11. 2025U-Codec: Ultra Low Frame-rate Neural Speech Codec for Fast High-fidelity Speech Generationcited by 1
  12. 2022Fully Convolutional Fractional Scalingcited by 0
  13. 2024JenGAN: Stacked Shifted Filters in GAN-Based Speech Synthesiscited by 0
  14. 2026S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantizationcited by 0
  15. 2026How Much AI Is in This Track? Quantifying the Proportion of AI-Generated Stems in Hybrid Music Mixturescited by 0
  16. 2026Tracing Decoder Artifacts for Compact Synthetic Speech Screeningcited by 0