Signal Adaptive Q Factor Selection for Resonance Based Signal Separation Using Tunable-Q Wavelet Transform

Abstract

Tunable Q wavelet transform (TQWT) was recently proposed as an efficient wavelet decomposition method which can match to the oscillatory behaviour of the signal. The selection of Q-factor is an important issue in obtaining a sparser signal representation by TQWT. Morphological component analysis (MCA) is a signal separation method which uses the tuning property of TQWT by selecting a low and a high Q-factor matches the signal components. However, the Q-factors are usually chosen experimentally or using the prior information. Thus, in this study, a signal adaptive Q-factor selection method which can be used with TQWT based analysis was proposed. The performance of the proposed algorithm is illustrated with two examples using MCA signal separation.