The decomposition of a multicomponent non-stationary signal is helpful in obtaining its time-frequency distribution (TFD). In this paper, a novel empirical mode decomposition (EMD) like eigenvalue decomposition of Hankel matrix (EVDHM) technique is proposed, which extracts the mono-component signal iteratively. In each iteration, EVDHM is performed on the residue signal to obtain elementary components (ECs). Later, the most dominant mono-component is extracted from the obtained ECs using a novel parameter proposed namely, envelope of the normalized instantaneous amplitude modulated bandwidth. The separability conditions for the two sinusoidal multicomponent signal are studied for the proposed decomposition technique using the average correlation measure. Finally, the proposed EMD-like EVDHM’s performance for signal decomposition is compared with iterative EVDHM and EMD technique for synthetic and real-world speech signal. Signal to reconstruction error (SRE) ratio is used as a performance measure and the proposed technique is found to be giving highest SRE value for all the considered synthetic signals when compared with the baselines. Further, the proposed technique is also found to be providing highest-resolution TFD for speech signal among all the techniques studied in this paper.

In this letter, a new framework based on Fourier- Bessel series expansion (FBSE)-empirical wavelet transform (EWT) is proposed with the aim of analyzing complex signals. The critical part of this framework is to separate the real- time equivalent positive and negative frequency components of complex signals using suitable filters and then decompose both the real-time signals into their corresponding intrinsic mode functions (IMFs) using the FBSE-EWT technique. After that, the obtained IMFs are represented in a set of complex IMFs (CIMFs) using the Hilbert transform. In the end, joint time-frequency distribution (TFD) is obtained using Hilbert spectral analysis of CIMFs. Two synthetic multicomponent complex signals are considered for simulation purposes, along with a real-world wind signal. The results of proposed framework compared with complex empirical mode decomposition (CEMD) and complex iterative eigenvalue decomposition of Hankel matrix (CIEVDHM) and found to be providing a better result.