Blind Speech Extraction Based on Rank-Constrained Spatial Covariance Matrix Estimation With Multivariate Generalized Gaussian Distribution

In this article, we propose a new blind speech extraction (BSE) method that robustly extracts a directional speech from background diffuse noise by combining independent low-rank matrix analysis (ILRMA) and efficient rank-constrained spatial covariance matrix (SCM) estimation. To achieve more accura...

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Veröffentlicht in:IEEE/ACM transactions on audio, speech, and language processing Jg. 28; S. 1948 - 1963
Hauptverfasser: Kubo, Yuki, Takamune, Norihiro, Kitamura, Daichi, Saruwatari, Hiroshi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Acceleration
Background noise
Blind speech extraction
Computational modeling
Computer simulation
Covariance matrices
Covariance matrix
diffuse noise
Estimation
Gaussian distribution
Mathematical models
Matrix methods
Methods
Multivariate analysis
multivariate complex generalized Gaussian distribution
Normal distribution
Optimization
Parameter estimation
Signal processing
spatial covariance matrix
Speech processing
Speech recognition
Statistical analysis
ISSN:2329-9290, 2329-9304
Online-Zugang:Volltext
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Zusammenfassung:In this article, we propose a new blind speech extraction (BSE) method that robustly extracts a directional speech from background diffuse noise by combining independent low-rank matrix analysis (ILRMA) and efficient rank-constrained spatial covariance matrix (SCM) estimation. To achieve more accurate BSE than ILRMA, which assumes each source to be a point source (rank-1 spatial model), the proposed method restores the lost spatial basis for the full-rank SCM of diffuse noise. We adopt the multivariate complex generalized Gaussian distribution (GGD) as the statistical generative model to express various types of observed signal. To estimate the model parameters for an arbitrary shape parameter of the multivariate GGD, we derive a new inequality for rank-constrained SCMs. Also, we propose new acceleration methods to accomplish much faster extraction than conventional blind source separation methods. In BSE experiments using simulated and real recorded data, we confirm that the proposed method achieves more accurate and faster speech extraction than conventional methods.
Bibliographie:ObjectType-Article-1
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ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2020.3003165