2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| Paper ID | MLSP-10.3 | ||
| Paper Title | UPSAMPLING ARTIFACTS IN NEURAL AUDIO SYNTHESIS | ||
| Authors | Jordi Pons, Santiago Pascual, Giulio Cengarle, Joan Serrà, Dolby Laboratories, Spain | ||
| Session | MLSP-10: Deep Learning for Speech and Audio | ||
| Location | Gather.Town | ||
| Session Time: | Tuesday, 08 June, 16:30 - 17:15 | ||
| Presentation Time: | Tuesday, 08 June, 16:30 - 17:15 | ||
| Presentation | Poster | ||
| Topic | Machine Learning for Signal Processing: [MLR-MUSAP] Applications in music and audio processing | ||
| IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
| Abstract | 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. | ||