DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
Abstract
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.
Cite
@article{arxiv.2305.08227,
title = {DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement},
author = {Hendrik Schröter and Tobias Rosenkranz and Alberto N. Escalante-B. and Andreas Maier},
journal= {arXiv preprint arXiv:2305.08227},
year = {2023}
}
Comments
Accepted as show and tell demo to interspeech 2023