DeepFilterNet: Perceptually Motivated Real-Time Speech Enhancement
This provides efficient speech enhancement for real-time applications, though it is incremental as it builds on existing deep filtering methods.
The authors tackled real-time speech enhancement by introducing DeepFilterNet, which matches state-of-the-art benchmarks with a real-time factor of 0.19 on a single-threaded notebook CPU.
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.