Paper Info
Title
Independent deeply learned matrix analysis with automatic selection of stable microphone-wise update and fast sourcewise update of demixing matrix.
Abstract
Independent deeply learned matrix analysis (IDLMA) is a fast and high-performance method for multi-channel audio source separation. IDLMA utilizes the deep neural network inference of source models and the blind estimation of demixing filters based on source independence. In conventional IDLMA, iterative projection (IP) is exploited to estimate the demixing filters. Although IP is a fast algorithm, it sometimes fails to estimate an appropriate solution. This is because IP updates the demixing filters in a sourcewise manner, where only one source model is used for each update, and the update sometimes becomes unstable owing to the specific low-quality source models. In this paper, we first derive a new numerically stable microphone-wise update algorithm that exploits all source model information simultaneously. The microphone-wise update problem cannot be solved by IP; instead, a new type of vectorwise coordinate descent algorithm is introduced. Next, comparison analysis of the proposed microphone-wise update and IP reveals the tradeoff w.r.t. convergence speed and numerical stability. To resolve this tradeoff problem, we propose the automatic selection of update rules on the basis of the likelihood function of observed signals. Finally, experimental results show the efficacy of the proposed IDLMA with the automatic selection of update rules. (C) 2020 The Author(s). Published by Elsevier B.V.
Year
DOI
Venue
2021
10.1016/j.sigpro.2020.107753
SIGNAL PROCESSING
Keywords
DocType
Volume
Audio source separation,Deep neural network,Independent deeply learned matrix analysis
Journal
178
ISSN
Citations 
PageRank 
0165-1684
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Naoki Makishima100.34
Yoshiki Mitsui221.74
Norihiro Takamune33510.18
Daichi Kitamura414221.21
Saruwatari, H.565290.81
Yu Takahashi611720.42
Kazunobu Kondo79318.13