  In this paper, we address a multichannel audio source separation task and
propose a new efficient method called independent deeply learned matrix
analysis (IDLMA). IDLMA estimates the demixing matrix in a blind manner and
updates the time-frequency structures of each source using a pretrained deep
neural network (DNN). Also, we introduce a complex Student's t-distribution as
a generalized source generative model including both complex Gaussian and
Cauchy distributions. Experiments are conducted using music signals with a
training dataset, and the results show the validity of the proposed method in
terms of separation accuracy and computational cost.
