Nonlinearities are often encountered in the analysis and processing of real-world signals. In this paper, we introduce two new structures for nonlinear signal processing. The new structures simplify the analysis, design, ...

The problem of speaker detection in audio databases is addressed in this paper. Gaussian mixture modeling is used to build target speaker and background models. A detection algorithm based on a likelihood ratio calculation ...

In this paper, we propose a simple framework for studying certain distributions of variables beyond time-frequency and time-scale. When applicable, our results turn the theory of joint distributions of arbitrary variables ...

Recent work in terahertz "T-Ray" imaging is reported. With the ongoing development of commercially viable THz-TDS imaging system, opitmail signal processing strategies for the THz waveforms must be developed. Algorithms ...

We introduce a new method for the time-scale analysis of non-stationary signals. Our work leverages the success of the "time-frequency distribution series / cross-term deleted representations" into the time-scale domain ...

1/<i>f</i> noise and statistically self-similar processes such as fractional Brownian motion (fBm) are vital for modeling numerous real-world phenomena, from network traffic to DNA to the stock market. Although several ...

This paper develops new algorithms for adapted multiscale analysis and signal adaptive wavelet transforms. We construct our adaptive transforms with the <i>lifting scheme</i>, which decomposes the wavelet transform into ...

The multifractal spectrum characterizes the scaling and singularity structures of signals and proves useful in numerous applications, from network traffic analysis to turbulence. Of great concern is the estimation of the ...

In this paper, we propose a formalism, called vector filtering of spectral trajectories, which allows to integrate under a common formalism a lot of speech parameterization approaches. We then propose a new filtering in ...

This paper describes a new technique, called <i>Empirical Mode Decomposition</i> (EMD), which allows the decomposition of one-dimensional signals into intrinsic oscillatory modes. Each component, called <i>Intrinsic Mode ...