DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement

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...

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Bibliographic Details
Published inarXiv.org
Main Authors Schröter, Hendrik, Rosenkranz, Tobias, Escalante-B, Alberto N, Maier, Andreas
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.05.2023
Subjects
Algorithms
Real time
Speech processing
Time factors
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Summary: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.
ISSN:2331-8422