Research on reconstruction and processing methods for complex acoustic signals

In complex environments, target acoustic signals are susceptible to interference from background noise during propagation, resulting in the signals captured by sensors being a mixture of valid acoustic emissions and noise. This significantly compromises the accuracy of target localization and damage assessment. To address the nonlinear and non-stationary characteristics of these signals, this paper proposes a novel denoising algorithm termed Improved Variational Mode Decomposition (IVMD)-Wavelet Reconstruction. The core innovation of our method lies in its fully adaptive framework: first, the Singular Value Decomposition (SVD) algorithm is employed to automatically determine the optimal number of intrinsic mode functions (IMFs), K , for the VMD, eliminating a critical manual parameter setting. Second, the correlation coefficient threshold method is utilized to select the optimal IMF components. Subsequently, the noise-dominant components are denoised using a wavelet reconstruction algorithm. Finally, the denoised components and the effective components are reconstructed to achieve the final denoised signal. We have conducted hundreds of experiments, which demonstrate that the proposed algorithm effectively denoises various types of signals and noises, yielding a low Root Mean Square Error (RMSE). Compared to the Ensemble Empirical Mode Decomposition (EEMD) threshold denoising algorithm, the Signal to Noise Ratio (SNR) of the signal processed by our method is increased by 28%, and the Mean Square Error (MSE) is reduced by 52%, which confirming its universality, superiority, and effectiveness.