A Spectrum Adaptive Segmentation Empirical Wavelet Transform for Noisy and Nonstationary Signal Processing

Abstract

Compared with thresholding methods based on the traditional wavelet transform (WT), empirical wavelet transform (EWT) has been demonstrated to outperform in terms of noise elimination by constructing an adaptive filter bank. However, as the state-of-the-art version of EWT, enhanced EWT (EEWT) requires that the number of components in the superposed signal as prior knowledge is known, which is impractical in reality and limits the application of this method. In this paper, a novel EWT that can adaptively estimate the number of components in the signal and achieve spectrum segmentation is proposed and is referred to as the spectrum adaptive segmentation empirical wavelet transform (SAS-EWT). Furthermore, a customized SAS-EWT for speech enhancement is proposed. According to the experimental results, our proposed SAS-EWT provides more accurate boundary detection and better denoising performance. The proposed method improves the performance by up to 5% in terms of PESQ, STOI, and SNR in comparison to EEWT.