  Independent deeply learned matrix analysis (IDLMA) is one of the
state-of-the-art multichannel audio source separation methods using the source
power estimation based on deep neural networks (DNNs). The DNN-based power
estimation works well for sounds having timbres similar to the DNN training
data. However, the sounds to which IDLMA is applied do not always have such
timbres, and the timbral mismatch causes the performance degradation of IDLMA.
To tackle this problem, we focus on a blind source separation counterpart of
IDLMA, independent low-rank matrix analysis. It uses nonnegative matrix
factorization (NMF) as the source model, which can capture source spectral
components that only appear in the target mixture, using the low-rank structure
of the source spectrogram as a clue. We thus extend the DNN-based source model
to encompass the NMF-based source model on the basis of the product-of-expert
concept, which we call the product of source models (PoSM). For the proposed
PoSM-based IDLMA, we derive a computationally efficient parameter estimation
algorithm based on an optimization principle called the
majorization-minimization algorithm. Experimental evaluations show the
effectiveness of the proposed method.
