Sliding eigenvalue decomposition-based cross-term suppression in Wigner–Ville distribution

Abstract

In this paper, a new method for the cross-term-free Wigner–Ville distribution (WVD) is proposed. The proposed method is based on sliding eigenvalue decomposition (SEVD) and the WVD which has been termed as SEVD–WVD. The SEVD decomposes the multicomponent signal into a set of monocomponent signals, and then, the WVD of analytic monocomponents is added to obtain a cross-term-free time–frequency representation. We have applied the proposed technique on clean and noisy synthetic signals in order to verify its robustness. Moreover, the SEVD–WVD is applied on speech signal to show its suitability on real-world data. The proposed method has been compared with other cross-term reduction techniques for the WVD in the literature. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.