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Acoustic reverberation introduces multipath components into an audio signal, and therefore changes the source signal statistical properties. This causes problems for source localisation and tracking since reverberation generates spurious peaks in the time delay functions, and makes the subsequent location estimator hard to track the motion trajectory.(More)
We propose a new image and blur prior model, based on non-stationary autoregressive (AR) models, and use these to blindly deconvolve blurred photographic images, using the Gibbs sampler. As far as we are aware, this is the first attempt to tackle a real-world blind image deconvolution (BID) problem using Markov chain Monte Carlo (MCMC) methods. We give(More)
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A Rao-Blackwellised particle filtering approach for tracking multiple simultaneously active and time-varying number of speakers is investigated. A novel measurement extraction method appropriate for the scenario of multiple sources is proposed based on a timefrequency masking technique, in which each source is represented separately by a joint gain-ratio(More)
In this paper we present a novel audio-visual speaker detection and localisation algorithm. Audio source position estimates are computed by a novel stochastic region contraction (SRC) audio search algorithm for accurate speaker localisation. This audio search algorithm is aided by available video information (stochastic region contraction with height(More)
In reverberant environments, a moving speaker yields a dynamically changing source-sensor geometry giving rise to a spatially-varying acoustic impulse response (AIR) between the source and sensor. Consequently, this leads to a time-varying convolutional relationship between the source signal and the observations and thus spectral colouration of the received(More)
In this paper we present a new solution to the problem of speaker tracking among people where occlusions occur (disappearance and non-speaking). In a normal conversation between two or more people, we learn speaker mel-cepstral coefficients (MFCC) and incorporate this information into a sequential Bayesian audio-video position tracker. The joint(More)