On Estimation of Time-Varying Variances of Source and Noise for Sensor Array Processing

Estimation of time-varying variances of signals for beamforming in sensor arrays is a challenging problem. Based on the assumption that the array manifold vector and the noise pseudo-coherence matrix are known a priori or are well estimated, we present in this paper two estimators for estimating the...

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Vydáno v:IEEE/ACM transactions on audio, speech, and language processing Ročník 28; s. 2865 - 2879
Hlavní autoři: Pan, Chao, Chen, Jingdong, Shi, Guangming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Array processing
Array signal processing
Arrays
Beamforming
Coherence
Covariance matrices
Estimation
Estimators
Gaussian mixture model (GMM)
Indexes
Manifolds
Mathematical analysis
Matrix algebra
Matrix methods
Noise
Parameter estimation
Sensor arrays
Signal processing
Signal to noise ratio
Speech processing
variance estimation
ISSN:2329-9290, 2329-9304
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Shrnutí:Estimation of time-varying variances of signals for beamforming in sensor arrays is a challenging problem. Based on the assumption that the array manifold vector and the noise pseudo-coherence matrix are known a priori or are well estimated, we present in this paper two estimators for estimating the time-varying variances of the source signal of interest and the noise. These two estimators are then extended to deal with the following situations: 1) there are multiple candidates of the noise pseudo-coherence matrix or the noise pseudo-coherence matrix is a linear combination of some base pseudo-coherence matrices, and 2) the estimation variance is large and smoothing is needed. Simulations for speech enhancement applications are performed and the results show that the proposed estimators can well track the time-varying variances of both the speech and noise signals. It is also demonstrated that the optimal beamformer using the variance parameters estimated with the presented estimators outperforms the widely used traditional optimal beamformers in terms of improvement in both the signal-to-noise ratio (SNR) and the log-spectral distortion (LSD).
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ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2020.3031376