We present AERO, a audio super-resolution model that processes speech and music signals in the spectral domain. AERO is based on an encoder-decoder architecture with UNet like skip connections. We optimize the model using both time and frequency domain loss functions. Specifically, we consider a set of reconstruction losses together with perceptual ones in the form of adversarial and feature discriminator loss functions. To better handle phase information the proposed method operates over the complex-valued spectrogram using two separate channels. Unlike prior work which mainly considers low and high frequency concatenation for audio super-resolution, the proposed method directly predicts the full frequency range. We demonstrate high performance across a wide range of sample rates considering both speech and music. AERO outperforms the evaluated baselines considering Log-Spectral Distance, ViSQOL, and the subjective MUSHRA test. Audio samples and code are available Blue here.
|Title of host publication
|ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Number of pages
|Published - 2023
|48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 2023 → 10 Jun 2023
|ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
|48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
|4/06/23 → 10/06/23
Bibliographical notePublisher Copyright:
© 2023 IEEE.
- audio super-resolution
- bandwidth extension
- speech synthesis