earticle

영어

A data adaptive approach to spectral analysis of audio signals is implemented in this paper. The audio signals are non-stationary as well as non-linear in nature and the traditional Fourier based spectral representation is not effective. The Hilbert spectral analysis implemented by noise assisted bivariate empirical mode decomposition (NA-BEMD) is introduced here as an efficient spectral representation scheme of audio signals. In BEMD, the fractional Gaussian noise (fGn) and analyzing speech signal are used as two separate variables. Both signals are decomposed together yielding a finite set of intrinsic mode functions (IMFs) for individual variables (signals). The use of fGn implements BEMD with dyadic filterbank characteristics. The instantaneous frequencies of individual IMFs are computed by applying Hilbert transform and then the time-frequency representation is achieved by arranging the energy with respect to time and frequency simultaneously. Such representation is called Hilbert spectrum (HS) which is analogous to spectrogram. The marginal HS derived from HS corresponds the total energy at each frequency component. The experimental results show that the Hilbert spectral analysis provides better representation of audio signal contents compared to the Fourier based approach.