Deduction model for low-frequency sound field of anechoic tank based on cLSTM-AM

To address the challenges of reverberation and low measurement accuracy under non-free field conditions in the low-frequency sound field measurement of anechoic tank, a long short-term memory network model (cLSTM-AM) with convolution layer and attention mechanism is proposed to realize the accurate deduction of acoustic radiation and acoustic scattering characteristics of the cylindrical shell structure in the low frequency band. Initially, the paper delineates the fundamental principles of the cLSTM-AM model, the components and operations of the convolutional layer and the attention mechanism embedding model, and the indicators for evaluating the performance of the deduction model. Subsequently, a methodology combining the simulation with the background noise of underwater test is used to meet the free field conditions of the ideal anechoic tank test, and the sound pressure data set of the deduced model is obtained. Secondly, the cLSTM-AM model with convolution layer and attention mechanism, the LSTM-AM model without convolution layer and the cLSTM model without attention mechanism are compared and evaluated. It is found that the cLSTM-AM model among them has the best prediction performance. After the parameter adjustment and optimization, the mean absolute error (MAE), root mean square error (RMSE) and Pearson correlation coefficient(COR) achieved by using the cLSTM-AM model are 0.019, 0.035 and 0.933, respectively, which verifies that the method has a high accuracy in deducing the acoustic radiation characteristics of cylindrical shells from very low frequency to low frequency in anechoic tank. Finally, the data obtained by using the cLSTM-AM model training are compared with the acoustic scattering experimental data of the cylindrical shell in the anechoic tank, which proves that the low-frequency deduction model is also effective for the acoustic scattering characteristics. After the parameter optimization, the MAE, RMSE and Pearson correlation coefficient are 0.008, 0.012 and 0.993, respectively.