Comparative Analysis of Wavelet Transform, EMD, and EMD-Wavelet Hybrid Methods for Time-Frequency Denoising

Comparative analysis of various time-frequency denoising methods represents a significant research domain in signal processing. Beyond the commonly employed techniques of Wavelet Transform, Empirical Mode Decomposition (EMD), and their hybrid combination, numerous alternative approaches warrant exploration. These may include statistical-based methods, machine learning approaches, and deep learning techniques for signal denoising. Through systematic evaluation and comparison of these methodologies, we can better understand their respective advantages and limitations in time-frequency denoising applications. Code implementations typically involve key functions such as MATLAB's wavedec for wavelet decomposition, emd for empirical mode decomposition, and custom thresholding algorithms for noise removal. The hybrid EMD-Wavelet approach often combines intrinsic mode functions (IMFs) from EMD with wavelet thresholding techniques, implemented through sequential processing pipelines. Further research should encompass theoretical analysis of different methods, algorithmic improvements focusing on computational efficiency and denoising performance, and extensive experimental validation across diverse signal types. Such investigations will contribute to enhancing the effectiveness and broadening the application scope of time-frequency denoising technologies in practical scenarios.