Deficient-basis-complementary rank-constrained spatial covariance matrix estimation based on multivariate generalized Gaussian distribution for blind speech extraction

Rank-constrained spatial covariance matrix estimation (RCSCME) is a blind speech extraction method utilized under the condition that one-directional target speech and diffuse background noise are mixed. In this paper, we propose a new model extension of RCSCME. RCSCME simultaneously conducts both th...

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Vydáno v:EURASIP journal on advances in signal processing Ročník 2022; číslo 1; s. 1 - 24
Hlavní autoři: Kondo, Yuto, Kubo, Yuki, Takamune, Norihiro, Kitamura, Daichi, Saruwatari, Hiroshi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 22.09.2022
Springer
Springer Nature B.V
SpringerOpen
Témata:
Algorithms
Background noise
Blind source separation
Blind speech extraction
Covariance matrix
Diffuse noise
Engineering
Gaussian processes
Mathematical models
Matrix methods
Normal distribution
Parameter estimation
Quantum Information Technology
Rank-constrained spatial covariance matrix estimation
Signal,Image and Speech Processing
Spatial covariance matrix
Speech recognition
Spintronics
Diffuse noise
Blind source separation
Spatial covariance matrix
Blind speech extraction
Rank-constrained spatial covariance matrix estimation
ISSN:1687-6180, 1687-6172, 1687-6180
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Shrnutí:Rank-constrained spatial covariance matrix estimation (RCSCME) is a blind speech extraction method utilized under the condition that one-directional target speech and diffuse background noise are mixed. In this paper, we propose a new model extension of RCSCME. RCSCME simultaneously conducts both the deficient rank-1 component complementation of the diffuse noise spatial covariance matrix, which is incompletely estimated by preprocessing methods such as independent low-rank matrix analysis, and the estimation of the source model parameters. In the conventional RCSCME, between the two parameters constituting the deficient rank-1 component, only the scale is estimated, whereas the other parameter, the deficient basis, is fixed in advance; however, how to choose the fixed deficient basis is not unique. In the proposed RCSCME model, we also regard the deficient basis as a parameter to estimate. As the generative model of an observed signal, we utilized the super-Gaussian generalized Gaussian distribution, which achieves better separation performance than the Gaussian distribution in the conventional RCSCME. Assuming the model, we derive new majorization-minimization (MM)- and majorization-equalization (ME)-algorithm-based update rules for the deficient basis. In particular, among innumerable ME-algorithm-based update rules, we successfully find an ME-algorithm-based update rule with a mathematical proof supporting the fact that the step of the update rule is larger than that of the MM-algorithm-based update rule. We confirm that the proposed method outperforms conventional methods under several simulated noise conditions and a real noise condition.
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ISSN:1687-6180
1687-6172
1687-6180
DOI:10.1186/s13634-022-00905-z