  Recent interest in exploiting Deep Learning techniques for Noise Suppression,
has led to the creation of Hybrid Denoising Systems that combine classic Signal
Processing with Deep Learning. In this paper, we concentrated our efforts on
extending the RNNoise denoising system (arXiv:1709.08243) with the inclusion of
complementary features during the training phase. We present a comprehensive
explanation of the set-up process of a modified system and present the
comparative results derived from a performance evaluation analysis, using a
reference version of RNNoise as control.
