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

ÖZKURT N.

41st International Conference on Telecommunications and Signal Processing (TSP), Athens, Greece, 4 - 06 July 2018, pp.767-770 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/tsp.2018.8441404
  • City: Athens
  • Country: Greece
  • Page Numbers: pp.767-770
  • Keywords: Morphological component analysis, tunable-Q wavelet transform, wavelet energy-entropy ratio
  • Dokuz Eylül University Affiliated: No

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.