Signal denoising optimization based on a Hilbert-Huang transform-triple adaptable wavelet packet transform algorithm

The Hilbert-Huang transform (HHT) can retain intrinsic signal characteristics after noise reduction but still leaves a slightly noisy signal, and the wavelet packet transform (WPT) denoising algorithm eliminates noise efficiently but causes distortion of the original signal. To overcome these issues, this paper proposes to combine these two algorithms linearly to maximize the signal-to-noise ratio (SNR) and increase the adaptive optimal solution for the three main steps involved in the WPT. The proposed algorithm is tested on voice signals with different background noise intensities and different noise functions in order to test the robustness of the new Hilbert-Huang transform triple adaptable wavelet packet transform (HHT-TAWPT) algorithm. The results prove that the proposed algorithm effectively denoises the signal while keeping the original signal intact and this was indicated by the segmental SNR and frequency spectrograms when compared to the individual HHT and WPT algorithms