  Multi-channel deep clustering (MDC) has acquired a good performance for
speech separation. However, MDC only applies the spatial features as the
additional information. So it is difficult to learn mutual relationship between
spatial and spectral features. Besides, the training objective of MDC is
defined at embedding vectors, rather than real separated sources, which may
damage the separation performance. In this work, we propose a deep attention
fusion method to dynamically control the weights of the spectral and spatial
features and combine them deeply. In addition, to solve the training objective
problem of MDC, the real separated sources are used as the training objectives.
Specifically, we apply the deep clustering network to extract deep embedding
features. Instead of using the unsupervised K-means clustering to estimate
binary masks, another supervised network is utilized to learn soft masks from
these deep embedding features. Our experiments are conducted on a spatialized
reverberant version of WSJ0-2mix dataset. Experimental results show that the
proposed method outperforms MDC baseline and even better than the oracle ideal
binary mask (IBM).
