Performance of Wavelet Transform and Empirical Mode Decomposition in Extracting Signals Embedded in Noise

Time-frequency transformations have gained increasing attention for the characterization of nonstationary signals in a broad spectrum of science and engineering applications. This study evaluates the performance of two popular transformations, the continuous wavelet transform and empirical mode decomposition with Hilbert transform EMD+HT , in estimating instantaneous frequency IF in the presence of noise. The findings demonstrate that under these conditions wavelets seeking harmonic similitude at various scales produce lower variance IF estimates than EMD+HT. The shortcomings of the latter approach are attributed to its empirical, envelopedependent nature, leading to bases that are themselves derived from noise. DOI: 10.1061/ ASCE 0733-9399 2007 133:7 849 CE Database subject headings: Spectral analysis; Stationary processes; Time series analysis; Frequency analysis; Transformations; Noise; Transient loads; Transient response.