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In this paper, the perceptually based loss functions for audio filtering used by Wolfe and Godsill [l] are shown to fit well within a complex-valued Support Vector Machine (SVM) framework. SVM regression is extended to estimation of complex-valued functions, including the derivation of a variant of the Sequential Minimal Optimisation (SMO) algorithm. Audio filters are derived using this based on an autoregressive (AR) model used for audio and two different Hermitian kernel functions. Results are found to be promising, and further improvements are discussed.

We propose a system that can reliably track multiple cars in congested traffic environments. Our system's key basis is the implementation of a sequential Monte Carlo algorithm, which introduces robustness against problems arising due to the proximity between vehicles. By directly modelling occlusions and collisions between cars we obtain promising results on an urban traffic dataset. Extensions to this initial framework are also suggested.

A geometric construction is presented which is shown to be an effective tool for understanding and implementing multi-category support vector classification. It is demonstrated how this construction can be used to extend many other existing two-class kernel-based classification methodologies in a straightforward way while still preserving attractive properties of individual algorithms. Reducing training times through incorporating the results of pairwise classification is also discussed and experimental results presented.