COMPLEX-VALUED SPATIAL AUTOENCODERS FOR MULTICHANNEL SPEECH ENHANCEMENT

In this contribution, we present a novel online approach to multichannel speech enhancement. The proposed method estimates the enhanced signal through a filter-and-sum framework. More specifically, complex-valued masks are estimated by a deep complex-valued neural network, termed the complex-valued spatial autoencoder. The proposed network is capable of manipulating both the phase and the amplitude of the microphone signals and hence, the network is able to exploit both spatial and spectral characteristics of the desired source signal resulting in a physically plausible spatial selectivity and superior speech quality.