This paper presents a set of cepstral parameters based on timevarying linear prediction. The lattice filter structure is utilized to accommodate efficient stabilization of models and a Bark-like warped frequency scale. As the proposed cepstral features are based on non-stationary spectral analysis there is a potential for complementary information not captured in conventional features. In class...

A Gabor or Weyl-Heisenberg frame for L2(Rd) is generated by time-frequency shifts of a square-integrable function, the Gabor atom, along a time-frequency lattice. The dual frame is again a Gabor frame, generated by the dual atom. In general, Gabor frames are not stable under a perturbation of the lattice constants; that is, even for arbitrarily small changes of the parameters the frame property...

By using the innovations process, this paper provides a unification and extension of the existing maximum a posteriori (MAP) detectors (MAPDs). The practically important topics of linear modulations, time-varying frequency-selective channels, differential phase detection, and fractional sampling are accounted for. The MAPDs are derived under different conditions of optimality and a priori knowl...

(57) ABSTRACT A software component for accessing and displaying time varying Internet content includes a de?nition for rendering a graphical user interface and a URL pointing to the time varying Internet content to be downloaded and presented within said user interface. A component provider may create displays of Internet content which vary as a function of the varying of the time-varying Inter...

OBJECTIVE Those who analyze EEG data require quantitative techniques that can be validly applied to time series exhibiting ranges of non-stationary behavior. Our objective is to introduce a new analysis technique based on formal non-stationary time series models. This novel method provides a decomposition of the time series into a set of 'latent' components with time-varying frequency content. ...

Due to the periodical motions of most machinery in steady state operation, many diagnosis techniques are based on frequency analysis. This is often performed through Fourier transform. Some extensions of these techniques to the more general case of non stationary operation have been proposed. They are based on signal processing advances such as time-frequency representations and adaptive filter...

This paper proposes an extension of the framework of linear time invariant system identification to linear slowly time-varying system identification. The class of systems considered is described by ordinary differential equations with coefficients varying piecewise polynomially with time. The estimation is performed in the frequency domain using multisine excitation signals within an errors-in-...

Beat-to-beat fluctuations in cardiovascular time series comprise different frequency components which can be employed to describe autonomic regulatory processes. The Exponential Distribution (ED) is presented here as a specific time-frequency distribution which has the potential to describe the time-related changes in the frequency content of these cardiovascular fluctuations. The ED has as adv...

A method of performing automatic classification of positive time-frequency distributions is presented. These distributions are computed via constrained optimization, minimizing the cross-entropy of the distribution subject to a set of constraints. An algorithm for clustering using cross-entropy as the distance measure between vectors was derived by Shore and Gray [14]. We apply this method to t...