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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| Veröffentlicht in: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) S. 806 - 810 |
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06.06.2021
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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. |
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| 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 |
| Author_xml | – sequence: 1 givenname: Yuto surname: Kondo fullname: Kondo, Yuto organization: The University of Tokyo,Tokyo,Japan – sequence: 2 givenname: Yuki surname: Kubo fullname: Kubo, Yuki organization: The University of Tokyo,Tokyo,Japan – sequence: 3 givenname: Norihiro surname: Takamune fullname: Takamune, Norihiro organization: The University of Tokyo,Tokyo,Japan – sequence: 4 givenname: Daichi surname: Kitamura fullname: Kitamura, Daichi organization: National Institute of Technology,Kagawa College,Kagawa,Japan – sequence: 5 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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