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 |
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Elsevier Ltd
15.02.2022
Elsevier BV Elsevier |
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| Abstract | •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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| AbstractList | •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. 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. |
| ArticleNumber | 108294 |
| Author | Li, Cong Antoni, Jerome Deng, Jiayu Yu, Liang Jiang, Weikang |
| Author_xml | – sequence: 1 givenname: Liang surname: Yu fullname: Yu, Liang organization: Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 200240 Shanghai, China – sequence: 2 givenname: Jerome surname: Antoni fullname: Antoni, Jerome organization: Université de Lyon, INSALyon, LVA, F-69621 Villeurbanne, France – sequence: 3 givenname: Jiayu surname: Deng fullname: Deng, Jiayu organization: Hagong Intelligent Robot Co., Ltd, 201100 Shanghai, China – sequence: 4 givenname: Cong surname: Li fullname: Li, Cong organization: Shanghai Hangyi Research Institute of High-tech Development, 200433 Shanghai, China – sequence: 5 givenname: Weikang surname: Jiang fullname: Jiang, Weikang email: wkjiang@sjtu.edu.cn organization: Institute of Vibration, Shock and Noise, State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 200240 Shanghai, China |
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| Keywords | Noise modeling Microphone array measurements Acoustic imaging Gaussian mixture model |
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| Snippet | •Space-snapshot representation for acoustic imaging in a complex, noisy environment is investigated.•Low rank and Mixture of Gaussian modeling are proposed to... The microphone array can simultaneously obtain the multi-dimensional information (space-time-frequency) of sound sources, which is recognized as a fundamental... |
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| SubjectTerms | 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 |
| Title | Low-rank Gaussian mixture modeling of space-snapshot representation of microphone array measurements for acoustic imaging in a complex noisy environment |
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