Automatic classification method for water entry sounds in multi-interference environments

In marine environments with strong pulse interference, distinguishing water entry sounds from pulse interference with high accuracy presents a challenge. Conventional algorithms that only utilize the singular values of intrinsic mode functions (IMFs) as the classification feature can have many misclassified signals in noisy environments. To identify the water entry sound more precisely, this study introduces a classifier based on selected singular value and correlation coefficient features. Specifically, the correlation coefficient between each IMF and the original signal are used to select crucial singular values and eliminate uninformative components. Furthermore, the correlation coefficient also combines with singular values as classification features for water entry sounds. The classification experiment results of 2460 groups of water entry sound and pulse interference indicate that the proposed classifier improves classification accuracy by approximately 11.5% compared to using singular values alone and by approximately 2.4% compared to classification accuracy without eliminating uninformative IMFs.