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Deep learning-based speech enhancement has seen huge improvements andrecently also expanded to full band audio (48 kHz). However, many approacheshave a rather high computational complexity and require big temporal buffersfor real time usage e.g. due to temporal convolutions or attention. Both makethose approaches not feasible on embedded devices. This work further extendsDeepFilterNet, which exploits harmonic structure of speech allowing forefficient speech enhancement (SE). Several optimizations in the trainingprocedure, data augmentation, and network structure result in state-of-the-artSE performance while reducing the real-time factor to 0.04 on a notebookCore-i5 CPU. This makes the algorithm applicable to run on embedded devices inreal-time. The DeepFilterNet framework can be obtained under an open sourcelicense.

DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio