Postfiltering Using an Adversarial Denoising Autoencoder with Noise-aware Training

An adversarial denoising autoencoder (ADAE) with noise-aware training is proposed and successfully applied to post-filtering for linear noise reduction. The ADAE is effective for attenuating interference sounds, however, it is difficult to learn to handle its various unexpected harmful effects (e.g., various types of noise) using a single network. Legacy speech enhancement was introduced as a pre-processor to make it possible to efficiently train the ADAEs by reducing the unexpected variabilities in the inputs to the ADAEs. Time-frequency masking performed well to suppress the variabilities, however, it induced unpleasant distortion, which is difficult for the ADAE to complement. In this paper, a minimum variance distortionless response (MVDR) beam-former, which can avoid troublesome non-linear distortions, is exploited as a preprocessor, and the MVDR outputs are used as the inputs to the ADAE-based post-filter. In addition, noise-dominant signals derived from the MVDR beamformer can improve the accuracy of the ADAE-based post-filter because the residual noise depends on the original noise signals. Experimental comparisons conducted using multichannel speech enhancement demonstrate that ADAE-based post-filtering yields significant improvements over the MVDR-and ADAE-based speech enhancement systems, and noise-aware training of ADAE works well.