25 accepted papers
This paper proposes a reversible jump Markov chain Monte Carlo method that provides efficient inference for the general problem of pulse fitting. In particular, it minimises the potential of an adopted parametric model overfitting to the (noisy) data via the inclusion of a peak proximity parameter.…
Multi-target tracking of agile targets can be limited by choice of dynamical models. This is typically overcome by using sophisticated non-Gaussian and/or nonlinear motion models, and complex data association schemes. Here we aim to tackle scenarios when tracking (semi-)autonomous systems, such as d…
Audio-visual tracking of multiple speakers requires to estimate the state (e.g. velocity and location) of each speaker by leveraging the information of both audio and visual modalities. Estimating the number of speakers and their states jointly remains a challenging problem. We propose an Audio-Visu…
Coordinate ascent variational inference (CAVI) is a popular approximate inference method; however, it relies on a mean-field assumption that can lead to large estimation errors for highly correlated variables. In this paper, we propose a conditionally factorized variational family with an adjustable…
Measurement rates for both targets and clutter have been assumed to be known a priori in most existing tracking systems, whereas practically the rates may be unknown to users or time-varying. This paper therefore fills this gap by developing a Poisson mixture process tracker (PMPT) to capture the te…
With the aim of addressing audio signal restoration as a sequential inference problem, we build upon Gabor regression to propose a state-space model for audio time series. Exploiting the structure of our model, we devise a sequential Markov chain Monte Carlo algorithm to explore the sequence of filt…
Parameter learning of the state-space model (SSM) plays a significant role in the modelling of time-series data and dynamical systems. However, the closed-form inference of the parameter posterior is often limited by sequential construction and non-linearity of the SSMs, which has led to the develop…
In group object tracking, the identification of the group leader can be highly beneficial for predicting the intention and future manoeuvres of objects as well as learning the underlying group behaviour traits. This paper presents an online approach for inferring dominant entities in tracked groups…
In this paper we present on-line Bayesian filtering methods for time series models corrupted by asymmetric Laplace noise. An optimum kernel particle filter is designed for the general asymmetric case, and its performance is compared to that of a traditional bootstrap filter and a newly designed Rao-…
In this paper, we introduce a generic Bayesian framework for inferring the intent of a tracked object, as early as possible, based on the available partial sensory observations. It treats the prediction problem, i.e. not estimating the object state such as position, within an object tracking formula…
In this paper we present Bayesian methods for tracking scenarios in which an intrinsic coordinate model is considered and inertial measurements plus occasional position fixes are available. The methods are first tested using synthetic data, giving a comprehensive evaluation as to their performance.…
In this paper we focus on Bayesian blind and semi-blind adaptive signal processing based on a broadband MIMO FIR model (e.g., for blind source separation (BSS) and blind system identification (BSI)). Specifically, we study in this paper a framework allowing us to systematically incorporate various t…
This paper presents a survey of the ideas behind the particle filtering, or sequential Monte Carlo, method, from at least 1930 up to the present day. The particle filter, which is now 25 years old, has been an immensely successful and widely used suite of methods for filtering and smoothing in state…
This paper investigates the on-line analysis of high-frequency financial order book data using Bayesian modelling techniques. Order book data involves evolving queues of orders at different prices, and here we propose that the order book shape is proportional to a gamma or inverse-gamma density func…
The Non-homogeneous Poisson process is a point process with time-varying intensity across its domain, the use of which arises in numerous areas in signal processing and machine learning. However, applications are largely limited by the intractable likelihood function and the high computational cost…
In this paper we extend to the multidimensional case the modified Poisson series representation of linear stochastic processes driven by α-stable innovations. The latter has been recently introduced in the literature and it involves a Gaussian approximation of the residuals of the series, via the ex…
In this paper we introduce a novel class of efficient multichannel adaptive filtering algorithms for sparse FIR systems. By suitably integrating ideas from compressed sensing and adaptive filter theory, this class of algorithms allows to significantly reduce the actual number of adaptive coefficient…
This paper proposes a probabilistic intent inference approach that is significantly more computationally efficient than other existing bridging-distributions-based predictors. It sequentially determines the probabilities of all possible destinations of a tracked object, whose motion is modelled by a…
Examining the dynamic aspects of functional networks in the brain is imperative in order to obtain a thorough description and to gain a better insight into its several features. Present methods of analysing brain data in task-conditions mainly include concatenation followed by temporal correlation.…
In many teleconferencing applications using modern laptop and net-book devices it is common to encounter annoying keyboard typing noise. In this paper we propose an acoustic keystroke transient canceler for speech communication terminals as a novel broadband adaptive filter application in such a han…
In this paper we present a Bayesian method for parameter estimation in linear Jump-Langevin systems, i.e. systems driven by a linear, mean-reverting jump-diffusion trend process. Such models have been applied successfully to trend following in finance, in order to develop momentum-based trading stra…
We propose a novel probabilistic inference approach that permits predicting, well in advance, the intended destination of a pointing gesture aimed at selecting an icon on an in-vehicle interactive display. It models the partial 3D pointing track as a Markov bridge terminating at a nominal destinatio…
In this paper a problem in transient noise suppression for audio streams in laptop and netbook devices is addressed. One or more microphones record voice signals which are corrupted with ambient noise and also transient noise from keyboard and mouse clicks. In the current work, a synchronous referen…
This paper presents an O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) time method for filtering and sampling of a time-varying n × n system matrix A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/x…
Recent empirical research has discovered that linkages among fMRI signals of the brain in resting-state have meaningful temporal variations. Most current studies of brain networks assume that these linkages are constant. We propose a model and an accompanying algorithm to infer and track changes in…