  Independent deeply learned matrix analysis (IDLMA) is one of the
state-of-the-art supervised multichannel audio source separation methods. It
blindly estimates the demixing filters on the basis of source independence,
using the source model estimated by the deep neural network (DNN). However,
since the ratios of the source to interferer signals vary widely among
time-frequency (TF) slots, it is difficult to obtain reliable estimated power
spectrograms of sources at all TF slots. In this paper, we propose an IDLMA
extension, empirical Bayesian IDLMA (EB-IDLMA), by introducing a prior
distribution of source power spectrograms and treating the source power
spectrograms as latent random variables. This treatment allows us to implicitly
consider the reliability of the estimated source power spectrograms for the
estimation of demixing filters through the hyperparameters of the prior
distribution estimated by the DNN. Experimental evaluations show the
effectiveness of EB-IDLMA and the importance of introducing the reliability of
the estimated source power spectrograms.
