Low-rank Gaussian mixture modeling of space-snapshot representation of microphone array measurements for acoustic imaging in a complex noisy environment

•Space-snapshot representation for acoustic imaging in a complex, noisy environment is investigated.•Low rank and Mixture of Gaussian modeling are proposed to achieve significant denoising performance.•Comparisons with other methods are made in simulations and experiments to verify the effect of the...

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Veröffentlicht in:Mechanical systems and signal processing Jg. 165; S. 108294
Hauptverfasser: Yu, Liang, Antoni, Jerome, Deng, Jiayu, Li, Cong, Jiang, Weikang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin Elsevier Ltd 15.02.2022
Elsevier BV
Elsevier
Schlagworte:
Acoustic imaging
Acoustics
Algorithms
Arrays
Beamforming
Factor analysis
Gaussian beams (optics)
Gaussian mixture model
Mechanics
Microphone array measurements
Noise
Noise measurement
Noise modeling
Physics
Principal components analysis
Probabilistic models
Probability density functions
Random noise
Representations
Sound sources
Statistical analysis
Noise modeling
Microphone array measurements
Acoustic imaging
Gaussian mixture model
ISSN:0888-3270, 1096-1216
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Zusammenfassung:•Space-snapshot representation for acoustic imaging in a complex, noisy environment is investigated.•Low rank and Mixture of Gaussian modeling are proposed to achieve significant denoising performance.•Comparisons with other methods are made in simulations and experiments to verify the effect of the state-of-the-art methods. The microphone array can simultaneously obtain the multi-dimensional information (space-time-frequency) of sound sources, which is recognized as a fundamental and powerful tool in acoustic imaging. Acoustic Beamforming is one of the widely used methods in acoustic imaging. However, most of the applications of beamforming are based on the Gaussian noise assumption, which is not always accurate in on-site measurements. For example, shock noise with a skewed probability density function (PDF) may appear on the signal record when the turbulent eddies are not controlled. Thus, in this paper, the conventional Gaussian noise model is extended to a Gaussian mixture noise model, which can approximate any probability distribution of the noise in theory. The space-snapshot representation of microphone array measurements is further modeled as a combination of the low-rank matrix part (measurements from the sound sources) and a Gaussian mixture matrix part (measurement noise). The signal from the sources of interest is finally recovered by the Expectation–maximization algorithm, which iterates between the low-rank approximation of the sound sources and the estimation of the parameter of the Gaussian mixture model. The proposed method is further investigated with simulations and compared with robust principal component analysis (RPCA) and Gaussian-based probabilistic factor analysis (PFA). It is concluded that the proposed method outperforms the state-of-the-art denoising methods.
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ISSN:0888-3270
1096-1216
DOI:10.1016/j.ymssp.2021.108294