4.7 Article

Acoustic Source Localization in a Reverberant Environment Based on Sound Field Morphological Component Analysis and Alternating Direction Method of Multipliers

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3077670

Keywords

Acoustic source localization; alternating direction method of multipliers (ADMM); morphological component analysis (MCA); plane-wave basis function; reverberant environment; sparse decomposition

Funding

  1. National Natural Science Foundation of China [12074254]
  2. Institute of Science and Technology on Underwater Test and Control
  3. State Key Laboratory of Mechanical System and Vibration [MSV202001]
  4. Science and Technology on Sonar Laboratory [6142109KF201901]
  5. State Key Laboratory of Compressor Technology [SKL-YSL201812, SKLYSJ201903]

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This article proposes a sound field morphological component analysis (SFMCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm for accurate acoustic source localization in a reverberant environment. Sparse representation of the acoustic signal in the frequency domain is achieved using dictionaries, and experiments validate the effectiveness of the proposed method.
The acoustic imaging results in the diffuse field are seriously affected by reverberation. The acoustic source localization algorithms based on the free-field assumption will produce many artifacts in the reverberant environment. To achieve accurate acoustic source localization in a reverberant environment, a sound field morphological component analysis (SFMCA) model and an enhanced alternating direction method of multipliers (ADMM) algorithm are proposed in this article. First, the solution of the inhomogeneous Helmholtz equation is analyzed to characterize the acoustic components. Second, Green's function and plane-wave basis function serve as dictionaries for sparse representation of the acoustic signal in the frequency domain, and the corresponding decomposition coefficients are obtained by the enhanced ADMM algorithm. Finally, the accurate acoustic imaging results are realized in the reverberant chamber space with strong reverberation (T-20 = 174.30 ms). Experiments demonstrate the validation of the proposed SF-MCA method. The acoustic imaging obtained by the proposed SF-MAC dereverberation model is far superior to the acoustic imaging that treats the reverberation as the general Gaussian noise.

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