3 accepted papers
We propose a complex-valued deep neural network (cDNN) for speech enhancement and source separation. While existing end-to-end systems use complex-valued gradients to pass the training error to a real-valued DNN used for gain mask estimation, we use the full potential of complex-valued LSTMs, MLPs a…
We propose binary neural networks (BNN s) for acoustic beamforming. This makes the speech enhancement approach resource efficient and applicable for embedded applications. Using CHiME4 data, we use BNN s to estimate the speech presence probability mask for GEV-PAN beamformers. By doing so, we achiev…
In this paper, we present an optimal multi-channel Wiener filter, which consists of an eigenvector beamformer and a single-channel postfilter. We show that both components solely depend on a speech presence probability, which we learn using a deep neural network, consisting of a deep autoencoder and…