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A blind source separation technique using second-order statistics
- A. Belouchrani, K. Abed-Meraim, J. Cardoso, É. Moulines
- EngineeringIEEE Trans. Signal Process.
- 1 February 1997
A new source separation technique exploiting the time coherence of the source signals is introduced, which relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices.
Log-Periodogram Regression Of Time Series With Long Range Dependence
This paper discusses the use of fractional exponential models (Robinson (1990), Beran (1994)) to model the spectral density f(x) of a covariance stationary process when f(x) may be decomposed as f(x)…
Continuous probabilistic transform for voice conversion
The design of a new methodology for representing the relationship between two sets of spectral envelopes and the proposed transform greatly improves the quality and naturalness of the converted speech signals compared with previous proposed conversion methods.
Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones
Convergence of a stochastic approximation version of the EM algorithm
The stochastic approximation EM (SAEM), which replaces the expectation step of the EM algorithm by one iteration of a stochastics approximation procedure, is introduced and it is proved that, under mild additional conditions, the attractive stationary points of the SAEM algorithm correspond to the local maxima of the function.
Inference in hidden Markov models
- O. Cappé, É. Moulines, T. Rydén
- Computer Science, MathematicsSpringer series in statistics
- 1 December 2010
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
Subspace methods for the blind identification of multichannel FIR filters
- É. Moulines, P. Duhamel, J. Cardoso, S. Mayrargue
- Computer ScienceProceedings of ICASSP '94. IEEE International…
- 19 April 1994
A class of methods for identifying a single input/multiple output finite impulse response system (SIMO-FIR), from the outputs of the system only, that provide significantly better estimates than the method by Tong et al. (1991), while requiring about one half the number of computations.
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
This work provides a non-asymptotic analysis of the convergence of two well-known algorithms, stochastic gradient descent as well as a simple modification where iterates are averaged, suggesting that a learning rate proportional to the inverse of the number of iterations, while leading to the optimal convergence rate, is not robust to the lack of strong convexity or the setting of the proportionality constant.
On‐line expectation–maximization algorithm for latent data models
A generic on‐line version of the expectation–maximization (EM) algorithm applicable to latent variable models of independent observations that is suitable for conditional models, as illustrated in the case of the mixture of linear regressions model.