Neural network for speech denoising trained with deep feature losses

Abstract

Techniques are provided for speech denoising using a denoising neural network (NN) trained with deep feature losses obtained from an audio classifier NN. A methodology implementing the techniques according to an embodiment includes applying the speech denoising NN, to be trained, to a noisy sample of a training speech signal to generate a processed training speech signal. The method further includes applying a trained audio classifier NN to the processed training speech signal to generate a first set of activation features, and applying the trained audio classifier NN to a clean sample of the training speech signal to generate a second set of activation features. The method further includes calculating a loss value based on the first and second sets of activation features, and performing a back-propagation training update of the denoising NN, based on the loss value. The method includes iterating this process to further train the denoising NN.