James R. Hopgood

Learn More
Room reverberation introduces multipath components into an audio signal and causes problems for acoustic source localization and tracking. Existing tracking methods based on the extended Kalman filter (EKF) and sequential importance resampling based particle filter (SIR-PF) usually assume that a single source is constantly active in the tracking scene.(More)
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)
Considering that multiple talkers may appear simultaneously, a time-frequency (TF) masking based random finite set (RFS) particle filtering (PF) method is developed for multiple acoustic source detection and tracking. The time-delay of arrival (TDOA) measurements of multiple sources are extracted by using a time-frequency masking technique, by which each(More)
Room reverberation leads to reduced intelligibility of audio signals and spectral coloration of audio signals. Enhancement of acoustic signals is thus crucial for high-quality audio and scene analysis applications. Multiple sensors can be used to exploit statistical evidence from multiple observations of the same event to improve enhancement. Whilst(More)
Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the signals can be separated. The paper shows that when signals are separated using the generalised Wiener filter, the degree of separability can be deduced from the filter structure. To identify this structure, the processes are(More)
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)
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)