A 3D convolutional neural network based near-field acoustical holography method with sparse sampling rate on measuring surface

Abstract

Near-field acoustical holography (NAH) is an efficient noise diagnosis method with the deficiencies including wraparound error, which will increase when the spatial sampling rate is reduced below the minimum specified by Shannon-Nyquist theorem. Based on 3D convolutional neural network (3D-CNN) and stacked autoencoder (SAE), a method called CSA-NAH is proposed to reduce the wraparound error under sparse measuring. Subsequently, numerical calculations are carried out to illustrate the feasibility and performance of CSA-NAH. The results show that when holographic measurement point number is 64, average reconstruction error of CSA-NAH on an aluminum plate for sound pressure within 2000 Hz is 4.32%, while the latest existing methods is greater than 10%. For error in 1200 Hz~2000 Hz, the error is reduced from more than 15% of the existing methods to 5.5%. Therefore, the application of the proposed CSA-NAH can cut down the measuring cost by reducing the number of microphones without wraparound error.