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Publications Influence

Sequential Monte Carlo smoothing for general state space hidden Markov models

- R. Douc, A. Garivier, E. Moulines, J. Olsson
- Mathematics
- 1 December 2011

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

135 21- PDF

Regularization techniques for discrete cepstrum estimation

- O. Cappé, E. Moulines
- Mathematics
- IEEE Signal Processing Letters
- 1 April 1996

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 this… Expand

73 14

Wavelet Estimator of Long-Range Dependent Processes

- J. Bardet, Gabriel Lang, E. Moulines, P. Soulier
- Mathematics
- 2000

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. This… Expand

83 12- PDF

Regularized estimation of cepstrum envelope from discrete frequency points

- O. Cappé, J. Laroche, E. Moulines
- Computer Science
- Proceedings of Workshop on Applications of…
- 15 October 1995

TLDR

78 11- PDF

Second Order Blind Separation of Temporally Correlated Sources

- A. Belouchrani, K. Abed-Meraim, Jean-François Cardoso, E. Moulines
- Mathematics
- 1993

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 can… Expand

105 10

Forgetting the initial distribution for Hidden Markov Models

- R. Douc, G. Fort, E. Moulines, P. Priouret
- Mathematics
- 28 March 2007

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 a… Expand

43 6- PDF

Subspace method for blind identification of multichannel FIR systems in noise field with unknown spatial covariance

- K. Abed-Meraim, Y. Hua, P. Loubaton, E. Moulines
- Mathematics
- IEEE Signal Processing Letters
- 1 May 1997

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 consider… Expand

30 5

Time-Domain and Frequency-Domain Techniques for Prosodic Modification of Speech

- E. Moulines, W. Verhelst
- Materials Science
- 1995

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 to… Expand

45 5

Bayesian Time Series Models: Adaptive Markov chain Monte Carlo: theory and methods

- Y. Atchadé, G. Fort, E. Moulines, P. Priouret
- Mathematics
- 2011

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 of… Expand

63 4- PDF

Scaling analysis of multiple-try MCMC methods

- M. Bédard, R. Douc, E. Moulines
- Mathematics
- 1 March 2012

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 of… Expand

34 3- PDF