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.

翻译：单通道语音增强的多帧算法能够利用语音信号中的短时相关性。深度滤波（Deep Filtering, DF）方法通过在频域直接估计复数滤波器来利用这些相关性。本文展示了基于DeepFilterNet的实时语音增强演示。DeepFilterNet的高效性得益于对语音产生机制和心理声学感知等先验知识的深度挖掘。我们的模型能够在单线程笔记本CPU上实现0.19的实时因子，同时达到与当前最先进的语音增强基准相媲美的性能。该框架及预训练权重已通过开源许可发布。