  This paper tackles the problem of the heavy dependence of clean speech data
required by deep learning based audio-denoising methods by showing that it is
possible to train deep speech denoising networks using only noisy speech
samples. Conventional wisdom dictates that in order to achieve good speech
denoising performance, there is a requirement for a large quantity of both
noisy speech samples and perfectly clean speech samples, resulting in a need
for expensive audio recording equipment and extremely controlled soundproof
recording studios. These requirements pose significant challenges in data
collection, especially in economically disadvantaged regions and for low
resource languages. This work shows that speech denoising deep neural networks
can be successfully trained utilizing only noisy training audio. Furthermore it
is revealed that such training regimes achieve superior denoising performance
over conventional training regimes utilizing clean training audio targets, in
cases involving complex noise distributions and low Signal-to-Noise ratios
(high noise environments). This is demonstrated through experiments studying
the efficacy of our proposed approach over both real-world noises and synthetic
noises using the 20 layered Deep Complex U-Net architecture.
