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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Vydané v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 806 - 810
Hlavní autori: Kondo, Yuto, Kubo, Yuki, Takamune, Norihiro, Kitamura, Daichi, Saruwatari, Hiroshi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.06.2021
Predmet:
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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Shrnutí: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.
ISSN:2379-190X
DOI:10.1109/ICASSP39728.2021.9414479