Instantaneous Frequency Selective Filtering Using Ensemble Empirical Mode Decomposition

In this paper, novel instantaneous frequency (IF) selective high-pass, low-pass and band-pass filtering techniques for multicomponent signals are developed using the ensemble empirical mode decomposition algorithm (EEMD). The EEMD algorithm is based on the property that the empirical mode decomposition algorithm acts on fractional Gaussian noise as a dyadic filter bank of constant-Q band-pass filters. Unlike pre-determined sub-band filtering, the filter-bank structure observed for EEMD applies locally to a signal. In the proposed techniques, frequency translation is carried out to ensure that the ratio of edge IFs of the pass-band and the stop-band is such that the EEMD algorithm will be able to distinguish between the components in the two bands. Also, EEMD approach is applied with band-limited white noise so that the signal components in the pass band and the stop band are extracted in different intrinsic mode functions. Simulations are used to demonstrate the efficacy of the proposed filtering algorithms and to compare their performance with conventional filtering techniques. The performance of the filters is assessed subjectively and in terms of objective criteria in presence of noise. The proposed filtering technique is also applied on a real speech signal to isolate speech resonance signals in accordance with the AM-FM model of speech.