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arXiv · 2305.08227

DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement

Hendrik Schröter·Tobias Rosenkranz·Alberto N. Escalante-B.·Andreas Maier
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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.

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BibTeXRIS

Hendrik Schröter, Tobias Rosenkranz, Alberto N. Escalante-B., Andreas Maier. 2023-05-14. DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement. https://arxiv.org/abs/2305.08227

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