  Despite noise suppression being a mature area in signal processing, it
remains highly dependent on fine tuning of estimator algorithms and parameters.
In this paper, we demonstrate a hybrid DSP/deep learning approach to noise
suppression. A deep neural network with four hidden layers is used to estimate
ideal critical band gains, while a more traditional pitch filter attenuates
noise between pitch harmonics. The approach achieves significantly higher
quality than a traditional minimum mean squared error spectral estimator, while
keeping the complexity low enough for real-time operation at 48 kHz on a
low-power processor.
