Time-Predictable Deep Noise Suppression on an Edge Device

Hearing aids and remote conference systems benefit from noise reduction. Current noise reduction approaches include machine-learning models that run on edge devices like hearing aids, AirPods, or headsets. Although not a safety-critical application, audio processing is a real-time application. We present a real-time enabled solution of speech enhancement with generation of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{C}$</tex> code for embedded devices, executing on a real-time processor, and analyzing the worst-case execution time for that application. Using the Patmos processor and the Platin WCET analysis tool, we can guarantee that we process noise canceling within the given deadline.