  Existing deep learning-based speech denoising approaches require clean speech
signals to be available for training. This paper presents a deep learning-based
approach to improve speech denoising in real-world audio environments by not
requiring the availability of clean speech signals in a self-supervised manner.
A fully convolutional neural network is trained by using two noisy realizations
of the same speech signal, one used as the input and the other as the output of
the network. Extensive experimentations are conducted to show the superiority
of the developed deep speech denoising approach over the conventional
supervised deep speech denoising approach based on four commonly used
performance metrics and also based on actual field-testing outcomes.
