  Over the past few years, speech enhancement methods based on deep learning
have greatly surpassed traditional methods based on spectral subtraction and
spectral estimation. Many of these new techniques operate directly in the the
short-time Fourier transform (STFT) domain, resulting in a high computational
complexity. In this work, we propose PercepNet, an efficient approach that
relies on human perception of speech by focusing on the spectral envelope and
on the periodicity of the speech. We demonstrate high-quality, real-time
enhancement of fullband (48 kHz) speech with less than 5% of a CPU core.
