Intel Corporation

Adobe, Inc.

Francois Georges Germain

Gautham J Mysore

Vladlen Koltun

Qifeng Chen

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.

Neural network for speech denoising trained with deep feature losses

Performance metric based stopping criteria for iterative algorithm techniques are described. In one or more implementations, a training dataset is processed by one or more computing devices using an iterative algorithm having a cost function. The processing includes, for a plurality of iterations of the iterative algorithm, computing a cost for the iterative algorithm using the cost function and a value for each of a plurality of performance metrics that are usable to infer accuracy of the iterative algorithm for a respective one of the iterations. Responsive to the processing, a particular one of the plurality of iterations is identified as a stopping criterion based at least in part on the computed values for the plurality of performance metrics and the stopping criterion is output to configure the iterative algorithm to use the stopping criterion for subsequent processing of data by the iterative algorithm.

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Stanford, CA, United States of America

Francois Georges Germain