DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Hendrik Schröter Tobias Rosenkranz Alberto N. Escalante-B. Andreas Maier
Multi-frame algorithms for single-channel speech enhancement are able to take
advantage from short-time correlations within the speech signal. Deep Filtering
(DF) was proposed to directly estimate a complex filter in frequency domain to
take advantage of these correlations. In this work, we present a real-time
speech enhancement demo using DeepFilterNet. DeepFilterNet's efficiency is
enabled by exploiting domain knowledge of speech production and psychoacoustic
perception. Our model is able to match state-of-the-art speech enhancement
benchmarks while achieving a real-time-factor of 0.19 on a single threaded
notebook CPU. The framework as well as pretrained weights have been published
under an open source license.