Speech processing using group delay functions
Signal Processing
IF and GD estimation from evolutionary spectrum
Signal Processing - Special section on Markov Chain Monte Carlo (MCMC) methods for signal processing
Signal Processing - Special issue on independent components analysis and beyond
Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay
Signal Processing - Signal processing in communications
Estimation of evolutionary spectrum based on short time Fourier transform and modified group delay
Signal Processing - Signal processing in communications
An automated feature extraction and emboli detection system based on the PCA and fuzzy sets
Computers in Biology and Medicine
IEEE Transactions on Signal Processing
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This paper proposes a new estimator for evolutionary spectrum (ES) based on short time Fourier transform (STFT) and modified group delay function (GDFM). The STFT due to its built-in averaging suppresses the crossterms and the GDFM preserves the frequency resolution of the rectangular window as it reduces the Gibbs ripple without using any window function. The new estimator is applicable to random signals as the GDFM removes the effect of the zeros due to input noise driving the time-varying system and provides the system information effectively. The GDFM also provides signal-to-noise ratio enhancement as it removes the zeros due to the associated noise. The performance of the method is illustrated for linear chirp signals, frequency shift keying and for time-varying random process which indicate that its frequency resolution is better than evolutionary periodogram (EP) and STFT and nearer to that of Wigner Ville distribution. Further, its noise immunity is better than those of EP and STFT.