EXPLORING DEEP COMPLEX NETWORKS FOR COMPLEX SPECTROGRAM ENHANCEMENT

Published in ICASSP, 2019

Abstract

A recent study has demonstrated the effectiveness of complexvalued deep neural networks (CDNNs) using newly developed tools such as complex batch normalization and complex residual blocks. Motivated by the fact that CDNNs are well suited for the processing of complex-domain representations, we explore CDNNs for speech enhancement. In particular, we train a CDNN that learns to map the complex-valued noisy short-time Fourier transform (STFT) to the clean STFT. Additionally, we propose the complex-valued extensions of the parametric rectified linear unit (PReLU) nonlinearity that helps to improve the performance of CDNN. Experimental results demonstrate that a CDNN using the proposed nonlinearity can give similar or better enhancement results compared to real-valued deep neural networks (DNNs). — Download here