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Sound Field Reconstruction Using Error Function Method
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AI
DOI：10.1109/JSEN.2024.3477473.png)
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Abstract
En 中文
The accurate sound field reconstruction, especially in medium- and low-frequency ranges, remains challenging due to limitations in traditional methods relying on the l(0) norm for sparsity promotion. These approaches are vulnerable to noise interference and may underestimate high-amplitude components, affecting reconstruction stability. In this work, we propose a novel sound field reconstruction method utilizing the error function (ERF) as a surrogate for the $\ell _{{0}}$ norm to improve both the speed and robustness of reconstruction. Comparative analysis with traditional methods, including the Tikhonov regularization method, monotonic two-step iterative shrinkage/thresholding (MTwIST) method, and l(1) -CVX, demonstrates the superior performance of the ERF method in terms of sound pressure accuracy and reconstruction reliability. Monte Carlo simulations, receiver operating characteristic (ROC) analysis, and experimental results further validate that the ERF method achieves improved localization and sound field reconstruction in the low-frequency domain, with better flexibility concerning measurement distance and signal-to-noise ratio (SNR).
Keywords:
Image reconstruction
Accuracy
Sensors
Microphone arrays
Convex functions
Minimization
Mathematical models
Computational modeling
Vectors
Reconstruction algorithms
Compressive sensing
error function (ERF)
& ell
p error
microphone array
sound field reconstruction
Journal
IF:
4.5
Papers:
2.1W
Citations:
7.3W
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