A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech

Understand

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.