Repairing distorted hologram data for sound field reconstruction

Area of Science:

• Acoustics and Signal Processing
• Computational Physics
• Data Science

Background:

• Sound field reconstruction relies on accurate acoustic measurements.
• Distorted or corrupted hologram data significantly degrades reconstruction accuracy.
• Existing methods may struggle with automated error detection and correction.

Purpose of the Study:

• To propose and validate a robust approach for repairing distorted hologram data.
• To enable accurate sound field reconstruction even with corrupted measurements.
• To develop an automated method for discriminating and correcting measurement errors.

Main Methods:

• An equivalent source model representing hologram pressures.
• Formulation within a modal framework using singular value decomposition.
• Bayesian inference to derive posterior distributions for measurement indicators and modal coefficients.

Main Results:

• Automated discrimination of all corrupted measurements.
• Accurate repair of distorted hologram pressures.
• Sound field reconstruction accuracy comparable to using error-free data.

Conclusions:

• The proposed method effectively repairs distorted hologram data for sound field reconstruction.
• The approach demonstrates robustness in both simulations and experimental validation.
• This technique enhances the reliability of acoustic measurements and reconstructions.