Deficient Basis Estimation of Noise Spatial Covariance Matrix for Rank-Constrained Spatial Covariance Matrix Estimation Method in Blind Speech Extraction

Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one r...

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Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 806 - 810
Main Authors: Kondo, Yuto, Kubo, Yuki, Takamune, Norihiro, Kitamura, Daichi, Saruwatari, Hiroshi
Format: Conference Proceeding
Language:English
Published: IEEE 06.06.2021
Subjects:
Acoustics
Analytical models
Blind speech extraction
Conferences
diffuse noise
EM algorithm
Estimation
Pareto optimization
Signal processing
Signal processing algorithms
spatial covariance matrix
ISSN:2379-190X
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Abstract Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one rank of the diffuse noise spatial covariance matrix, which cannot be estimated via preprocessing such as independent low-rank matrix analysis, and estimates the source model parameters simultaneously. In the conventional RC- SCME, a direction of the deficient basis is fixed in advance and only the scale is estimated; however, the candidate of this deficient basis is not unique in general. In the proposed RCSCM model, the deficient basis itself can be accurately estimated as a vector variable by solving a vector optimization problem. Also, we derive new update rules based on the EM algorithm. We confirm that the proposed method outperforms conventional methods under several noise conditions.
AbstractList Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional target speech and diffuse noise are mixed. In this paper, we proposed a new algorithmic extension of RCSCME. RCSCME complements a deficient one rank of the diffuse noise spatial covariance matrix, which cannot be estimated via preprocessing such as independent low-rank matrix analysis, and estimates the source model parameters simultaneously. In the conventional RC- SCME, a direction of the deficient basis is fixed in advance and only the scale is estimated; however, the candidate of this deficient basis is not unique in general. In the proposed RCSCM model, the deficient basis itself can be accurately estimated as a vector variable by solving a vector optimization problem. Also, we derive new update rules based on the EM algorithm. We confirm that the proposed method outperforms conventional methods under several noise conditions.
Author Takamune, Norihiro
Kubo, Yuki
Kitamura, Daichi
Saruwatari, Hiroshi
Kondo, Yuto
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  organization: National Institute of Technology,Kagawa College,Kagawa,Japan
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  givenname: Hiroshi
  surname: Saruwatari
  fullname: Saruwatari, Hiroshi
  organization: The University of Tokyo,Tokyo,Japan
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Snippet Rank-constrained spatial covariance matrix estimation (RCSCME) is a state-of-the-art blind speech extraction method applied to cases where one directional...
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SubjectTerms Acoustics
Analytical models
Blind speech extraction
Conferences
diffuse noise
EM algorithm
Estimation
Pareto optimization
Signal processing
Signal processing algorithms
spatial covariance matrix
Title Deficient Basis Estimation of Noise Spatial Covariance Matrix for Rank-Constrained Spatial Covariance Matrix Estimation Method in Blind Speech Extraction
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