Multiresolution decomposition techniques for robust signal processing

Abstract

Signal decomposition is particularly important for representing the signal components whose localization in time and frequency vary widely. The complexity of structures encountered in some signals requires adaptive low level decomposition. Signal decomposition finds applications in a wide range of areas such as signal compression, denoising, separation and extraction. This paper describes some of the tools developed for this type decomposition, starting with the short time Fourier transform (STFT) for basic decomposition, leading to the wavelet transform (WT) and matching pursuit (MP) for applications that are more sensitive and require details.