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Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). This paper aims at presenting forensic automatic speaker recognition (FASR) methods that provide ...
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A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian po ...
We consider the problem of positioning estimation with impulse radio (IR) ultra-wideband (UWB) radio under dense multipaths and additive Gaussian noise environments. Most popular positioning algorithms first estimate certain parameters (such as time of arr ...
Abstract: Map matching algorithms are the conventional way to generate path observations from GPS data for route choice models. The deterministic matching may introduce extra biases to parameters of route choice models if the matching is wrong. In this pap ...
Smartphones collect a wealth of information about their users. This includes GPS tracks and the MAC addresses of devices around the user, and it can go as far as taking visual and acoustic samples of the user's environment. We present a framework to identi ...
We consider the problem of ranging with Impulse Radio (IR) Ultra-WideBand (UWB) radio under weak Line Of Sight (LOS) environments and additive Gaussian noise. We use a Bayesian approach where the prior distribution of the channel follows the IEEE 802.15.4a ...
Multimodal signal processing analyzes a physical phenomenon through several types of measures, or modalities. This leads to the extraction of higher-quality and more reliable information than that obtained from single-modality signals. The advantage is two ...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinite ...
An algorithm is proposed for denoising the signal induced by cosmic strings in the cosmic microwave background (CMB). A Bayesian approach is taken, based on modeling the string signal in the wavelet domain with generalized Gaussian distributions. Good perf ...
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinite ...