EMD-based time-frequency analysis methods of audio signals

To ensure that any time series data is appropriately interpreted, it should be analyzed with proper signal processing tools. The most common analysis methods are kernel-based transforms, which use base functions and modifications to represent time series data. This work discusses an analysis of audio data and two of those transforms - the Fourier transform and the wavelet transform based on a priori assumptions about the signal\u27s linearity and stationarity. In audio engineering, these assumptions are invalid because the statistical parameters of most audio signals change with time and cannot be treated as an output of the LTI system. That is why recent approaches involve the decomposition of a signal into different modes in a data-dependent and adaptive way, which may provide advantages over kernel-based transforms. Such tools include empirical mode decomposition-based methods and Holo-Hilbert Spectral Analysis. Simulations were performed with speech signal for kernel-based and data-dependent decomposition methods, which revealed that evaluated decomposition methods are promising approaches to analyzing nonstationary and nonlinear audio data