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This paper presents a probabilistic model of temporal structure in music which allows joint inference of tempo, meter and rhythmic pattern. The framework of the model naturally quantifies these three musical concepts in terms of hidden state-variables, allowing resolution of otherwise apparent ambiguities in musical structure. At the heart of the system is(More)
Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the(More)
Switching state-space models (SSSM) are a popular class of time series models that have found many applications in statistics, econometrics and advanced signal processing. Bayesian inference for these models typically relies on Markov chain Monte Carlo (MCMC) techniques. However, even sophisticated MCMC methods dedicated to SSSM can prove quite inecient as(More)
We present efficient Monte Carlo algorithms for performing Bayesian inference in a broad class of models: those in which the distributions of interest may be represented by time marginals of continuous-time jump processes conditional on a realisation of some noisy observation sequence. The sequential nature of the proposed algorithm makes it particularly(More)
This paper addresses the problem of estimating the Potts-Markov random field parameter β jointly with the unknown parameters of a Bayesian image segmentation model. We propose a new adaptive Markov chain Monte Carlo (MCMC) algorithm for performing joint maximum marginal likelihood estimation of β and maximum-a-posteriori unsupervised image(More)
We introduce a general form of sequential Monte Carlo algorithm defined in terms of a pa-rameterized resampling mechanism. We find that a suitably generalized notion of the Effective Sample Size (ESS), widely used to monitor algorithm degeneracy, appears naturally in a study of its convergence properties. We are then able to phrase sufficient conditions for(More)
This paper presents a framework for the modelling of temporal characteristics of musical signals and an approximate, sequential Monte Carlo inference scheme which yields estimates of tempo and rhythmic pattern from onset-time data. These two features are quantified through the construction of a probabilistic dynamical model of a hidden 'bar-pointer' and a(More)
We consider a method for approximate inference in hidden Markov models (HMMs). The method circumvents the need to evaluate conditional densities of observations given the hidden states. It may be considered an instance of Approximate Bayesian Computation (ABC) and it involves the introduction of auxiliary variables valued in the same space as the(More)
Target–tracking problems involve the on-line estimation of the state vector of an object under surveillance, called a target, that is changing over time. The state of the target at time n, denoted X n , is a vector in E 1 ⊂ R d1 and contains its kinematic characteristics, e.g. the target's position and velocity. Typically only noise corrupted measurements(More)