Sound field reconstruction using block sparse Bayesian learning equivalent source method.

Area of Science:

• Acoustics
• Signal Processing
• Computational Physics

Background:

• Nearfield acoustic holography requires sparse bases for accurate sound field reconstruction with limited measurement points.
• Identifying appropriate sparse bases is challenging and source-dependent, necessitating complex manual construction.

Purpose of the Study:

• To develop a versatile method for sound field reconstruction applicable to diverse sound source types.
• To eliminate the need for elaborate, source-specific sparse basis construction in acoustic holography.

Main Methods:

• A block sparse Bayesian learning (SBL) equivalent source method was proposed.
• A block sparse equivalent source model was constructed with a structured prior.
• The posterior of the model was estimated using SBL, allowing reconstruction by adjusting block size.

Main Results:

• The method accurately reconstructs sound fields from spatially sparse, extended, and mixed sources.
• Numerical simulations and experimental results validated the method's effectiveness and superiority.
• The influence of block size and sparsity pruning threshold on reconstruction was investigated.

Conclusions:

• The proposed block sparse SBL equivalent source method offers a robust solution for sound field reconstruction across different source types.
• This approach simplifies the process by avoiding the need for manual sparse basis design.
• The method's performance is tunable via block size adjustment, demonstrating its practical applicability.