Feature Generation for Seismic and Acoustic Signals Based on Hilbert-Huang Transform

The successful detection and classification of events require using signal processing algorithms suitable for analysis of nonlinear and nonstationary signals like seismic and acoustic ones. A key point of the signal classification is to generate the feature vector by the means whose signals can be characterized and differentiated from another classes of signals. Some characteristics of seismic and acoustic signals can be described in the frequency and time-frequency domain by using adaptive signal processing methods like the Hilbert-Huang transform. The paper presents results of seismic and acoustic signal processing in the process of feature vector generation by detection of significant frequency components in the energy density spectrum of Hilbert-Huang transform.