Abstract
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 as reference in training mode. 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 target of the network. Two noisy realizations of the same speech signal are generated by using a mid-side stereo microphone. 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 as well as a subjective testing.
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