In spite of the interest in and appeal of convolution-based approaches for nonstationary spatial modeling, off-the-shelf software for model fitting does not as of yet exist. Convolution-based models are highly flexible yet notoriously difficult to fit, even with relatively small data sets. The general lack of pre-packaged options for model fitting makes it difficult to compare new methodology i...

Bilinear time-frequency representations (TFRs) and timescale representations (TSRs) are potentially very useful for detecting a nonstationary signal in the presence of nonstationary noise or interference. As quadratic signal representations, they are promising for situations in which the optimal detector is a quadratic function of the observations. All existing time-frequency formulations of qu...

This paper introduces new adaptive beamforming methods for nonstationary noise reduction, designed to be robust against broadband interfering signals. In particular, we propose combined beamforming schemes within a standard adaptive beamforming system, such as the generalized sidelobe canceller (GSC). The novelty of such combined adaptive beamformers relies on the use of different adaptive side...

Most of economic and financial time series have a nonstationary behavior. There are different types of nonstationary processes, such as those with stochastic trend and those with deterministic trend. In practice, it can be quite difficult to distinguish between the two processes. In this paper, we compare random walk and determinist trend processes using sample autocorrelation, sample partial a...

Many natural time series may have nonlinearity and nonstationarity. Although interpreting complex phenomena by the theory of nonlinear dynamical systems have been successful, it is still important to consider how to deal with nonstationarity in time series. In this paper, we analyze nonstationary time series by stationary versus nonstationary modeling in order to discuss the present issue.

In this article, nonstationary mixing and source models are combined for developing new fast accurate algorithms Independent Component or Vector Extraction (ICE/IVE), one of which stands a extension the well-known FastICA. This model allows moving source-of-interest (SOI) whose distribution on short intervals can be (non-)circular (non-)Gaussian. A particular Gaussian assuming tridiagonal covar...

Many phenomena, both natural and human influenced, give rise to signals whose statistical properties change under time translation, i.e., are nonstationary. For some practical purposes, a nonstationary time series can be seen as a concatenation of stationary segments. However, the exact segmentation of a nonstationary time series is a hard computational problem which cannot be solved exactly by...

Performance of automatic speech recognition relies on a vast amount of training speech data mostly recorded with little or no background noise. The performance degrades significantly with existence of background noise, which increases type mismatch between train and test environments. Speech enhancement techniques can reduce the amount of type mismatch by extracting reliable speech features fro...

Recent work in the areas of nonparametric regression and spatial smoothing has focused on modelling functions of inhomogeneous smoothness. In the regression literature, important progress has been made in fitting free-knot spline models in a Bayesian context, with knots automatically being placed more densely in regions of the covariate space in which the function varies more quickly. In the sp...