Optimized acoustic signal denoising using modified threshold shrinkage functions

Underwater noise is a significant issue that affects various underwater activities such as ocean exploration, submarine communication, SONAR detection, etc. De-noising stages are traditionally embedded in underwater activities; therefore, developing de-noising algorithms is a highly demanded field of study. In this paper, a suggested modification is applied to some well-known threshold functions that will be used in conjunction with complex wavelet transform (CWT). The modification is applied to the Garrote threshold function and two semi-soft threshold functions. CWT is used to decompose the signals, that are corrupted by measured underwater noise, then a modified wavelet shrinkage is applied to remove the noise and recover the original signal. The performance of the modified functions is compared with the original functions and the soft threshold function. The results demonstrate that the functions after the modification have better performance than the soft function and the original functions. The Garrote function has about 2.5 dB signal-to-noise ratio improvement and the semi-soft functions also have about 2 dB and 0.5 dB improvement respectively.