  Deep learning-based speech enhancement has seen huge improvements and
recently also expanded to full band audio (48 kHz). However, many approaches
have a rather high computational complexity and require big temporal buffers
for real time usage e.g. due to temporal convolutions or attention. Both make
those approaches not feasible on embedded devices. This work further extends
DeepFilterNet, which exploits harmonic structure of speech allowing for
efficient speech enhancement (SE). Several optimizations in the training
procedure, data augmentation, and network structure result in state-of-the-art
SE performance while reducing the real-time factor to 0.04 on a notebook
Core-i5 CPU. This makes the algorithm applicable to run on embedded devices in
real-time. The DeepFilterNet framework can be obtained under an open source
license.
