  Deep generative models for Speech Enhancement (SE) received increasing
attention in recent years. The most prominent example are Generative
Adversarial Networks (GANs), while normalizing flows (NF) received less
attention despite their potential. Building on previous work, architectural
modifications are proposed, along with an investigation of different
conditional input representations. Despite being a common choice in related
works, Mel-spectrograms demonstrate to be inadequate for the given scenario.
Alternatively, a novel All-Pole Gammatone filterbank (APG) with high temporal
resolution is proposed. Although computational evaluation metric results would
suggest that state-of-the-art GAN-based methods perform best, a perceptual
evaluation via a listening test indicates that the presented NF approach (based
on time domain and APG) performs best, especially at lower SNRs. On average,
APG outputs are rated as having good quality, which is unmatched by the other
methods, including GAN.
