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Sequential Monte Carlo smoothing for general state space hidden Markov models
Computing smoothing distributions, the distributions of one or more states conditional on past, present, and future observations is a recurring problem when operating on general hidden Markov models.Expand
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Regularization techniques for discrete cepstrum estimation
Traditional spectral envelope estimation methods suffer from significant drawbacks in (high-pitched) voiced segments: spectral peaks tend to be biased toward pitch harmonics. To alleviate thisExpand
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Wavelet Estimator of Long-Range Dependent Processes
In this contribution, the statistical properties of the wavelet estimator of the long-range dependence parameter introduced in Abry et al. (1995) are discussed for a stationary Gaussian process. ThisExpand
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Regularized estimation of cepstrum envelope from discrete frequency points
TLDR
This paper presents an improved method for the estimation of a continuous frequency-envelope when the value of this envelope is specified only at discrete frequencies. Expand
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Second Order Blind Separation of Temporally Correlated Sources
Blind separation of sources consists in recovering a set of statistically independent signals whose only mixtures are observed. Such instantaneous mixtures occur in narrow band array data which canExpand
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Forgetting the initial distribution for Hidden Markov Models
The forgetting of the initial distribution for discrete Hidden Markov Models (HMM) is addressed: a new set of conditions is proposed, to establish the forgetting property of the filter, at aExpand
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Subspace method for blind identification of multichannel FIR systems in noise field with unknown spatial covariance
We present a new subspace-based method for blind identification of multichannel finite impulse response (FIR) systems. Instead of assuming spatially white additive noise as commonly used, we considerExpand
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Time-Domain and Frequency-Domain Techniques for Prosodic Modification of Speech
An undercarriage for farm wagons and similar vehicles in which the frame of the vehicle is mounted on one or more axles by units at each end of the axles, each unit consisting of plates connected toExpand
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Bayesian Time Series Models: Adaptive Markov chain Monte Carlo: theory and methods
In general, the transition probability P of the Markov chain depends on some tuning parameter θ defined on some space Θ which can be either finite dimensional or infinite dimensional. The success ofExpand
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Scaling analysis of multiple-try MCMC methods
Multiple-try methods are extensions of the Metropolis algorithm in which the next state of the Markov chain is selected among a pool of proposals. These techniques have witnessed a recent surge ofExpand
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