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 U-Net 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 at
https://pages.cs.huji.ac.il/adiyoss-lab/aer