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This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the ti ...
An alternative way to process data from polarization measurements for fuel cell model validation is proposed. The method is based on re- and subsampling of IV data, with which repetitive estimations are obtained for the model parameters. This way statistic ...
Existing theory for multivariate extreme values focuses upon characterizations of the distributional tails when all components of a random vector, standardized to identical margins, grow at the same rate. In this paper, we consider the effect of allowing t ...
Bernoulli Society for Mathematical Statistics and Probability2013
HMM state mapping with the Kullback-Leibler divergence as a distribution similarity measure is a simple and effective technique that enables cross-lingual speaker adaptation for speech synthesis. However, since this technique does not take any other potent ...
An epidemic that spreads in a network calls for a decision on the part of the network users. They have to decide whether to protect themselves or not. Their decision depends on the trade-off between their perceived infection and the protection cost. Aiming ...
This paper offers a methodological contribution for computing the distance between two empirical distributions in an Euclidean space of very large dimension. We propose to use decision trees instead of relying on standard quantification of the feature spac ...
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time correlat ...
We develop a principled way of identifying probability distributions whose independent and identically distributed realizations are compressible, i.e., can be well approximated as sparse. We focus on Gaussian compressed sensing, an example of underdetermin ...
We propose a partition-based state estimator for linear discrete-time systems composed by coupled subsystems affected by bounded disturbances. The architecture is distributed in the sense that each subsystem is equipped with a local state estimator that ex ...
We develop tractable semidefinite programming based approximations for distributionally robust individual and joint chance constraints, assuming that only the first- and second-order moments as well as the support of the uncertain parameters are given. It ...
