nils-herrmann/clean_scirepeval huggingface dataset:
Particle filtering for non-stationary speech modelling and enhancement

This paper applies time-varying autoregressive (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. The stochastic evolution models for the TVAR parameters are Markovian di usion processes. The main aim of the paper is to perform on-line estimation of the clean speech and the model parameters, and to determine the adequacy of the chosen statistical models. An eÆcient simulationbased method is developed to solve the optimal ltering problem. The algorithm combines sequential importance sampling and a selection step, and employs several variance reduction strategies to make the best use of the statistical structure of the model. The modelling and enhancement performance of the model and algorithm are evaluated in simulation studies on real speech data sets.
Particle filters for demodulation of M-ary modulated signals in noisy fading communication channels

We address the problem of demodulation of M-ary modulated signals under conditions of noisy fading channels. The transmitted signal is corrupted in a random manner by a variety of possible mechanisms, and our aim is to recover the original message from the observations. This is a challenging non-linear filtering problem, and in order to solve it an efficient simulation-based algorithm, based on particle filters, is developed. The method combines sequential importance sampling, a selection scheme, Markov chain Monte Carlo (MCMC) methods and variance reduction techniques. Optimal fixed-lag smoothing is also considered. An application to the problem of demodulation of digital M-ary differential phase shift keyed (MDPSK) signals is presented and an extensive simulation study is carried out. The results show that the algorithm outperforms the current methods that are routinely used in most communication applications.
Simulated annealing for maximum a Posteriori parameter estimation of hidden Markov models

Hidden Markov models are mixture models in which the populations from one observation to the next are selected according to an unobserved finite state-space Markov chain. Given a realization of the observation process, our aim is to estimate both the parameters of the Markov chain and of the mixture model in a Bayesian framework. We present an original simulated annealing algorithm which, in the same way as the EM (expectation-maximization) algorithm, relies on data augmentation, and is based on stochastic simulation of the hidden Markov chain. This algorithm is shown to converge toward the set of maximum a posteriori (MAP) parameters under suitable regularity conditions.
Bayesian segmentation of piecewise constant autoregressive processes using MCMC methods

In this paper we address the problem of the Bayesian segmentation of signals which are modelled as piecewise constant autoregressive (AR) processes excited by white Gaussian noise. We define a probabilistic model that takes into account the fact that the number of AR processes needed to represent the data, the model orders, the values of the parameters and the noise variances are unknown. The resulting posterior probability distributions and Bayesian estimators of the model parameters do not admit closed-form analytical expression. A stochastic algorithm based on a reversible jump Markov chain Monte Carlo (MCMC) method [11] is derived to estimate these quantities. Results are obtained for both synthetic and real data (a speech signal which has been examined in the literature previously ,  and ) and confirm the applicability of both the model and the algorithm.  Andrieu is sponsored by AT&T Laboratories, Cambridge, UK. †A.Doucet is sponsored by EPSRC, UK.
Robust Full Bayesian Methods for Neural Networks

In this paper, we propose a full Bayesian model for neural networks. This model treats the model dimension (number of neurons), model parameters, regularisation parameters and noise parameters as random variables that need to be estimated. We then propose a reversible jump Markov chain Monte Carlo (MCMC) method to perform the necessary computations. We find that the results are not only better than the previously reported ones, but also appear to be robust with respect to the prior specification. Moreover, we present a geometric convergence theorem for the algorithm.
Non-stationary Bayesian modelling and enhancement of speech signals

This report applies time-varying AR (TVAR) models with stochastically evolving parameters to the problem of speech modelling and enhancement. For the TVAR coefficients the standard parameterisation, i.e. the coefficients of the TVAR polynomial themselves, and one i.t.o. the characteristic roots of the TVAR polynomial (or system poles) are investigated. The stochastic evolution models for the TVAR parameters are Markovian diffusion processes. The problem and estimation objectives are formulated within a Bayesian framework. Two efficient iterative algorithms are developed to achieve these objectives. The first is a Markov chain Monte Carlo (MCMC) algorithm which generates samples from the posterior distribution based on which the minimum mean square error (MMSE) estimates of the TVAR parameters and clean speech can be computed. The second is a stochastic optimisation algorithm which computes the marginal maximum a posteriori (MMAP) estimate of the TVAR parameters. The clean speech can then be obtained by running a fixed-interval Kalman smoother with this estimate of the TVAR parameters. Contrary to the EM-type algorithms, the estimation schemes work without introducing a set of " missing data " (the clean speech in this case). Nevertheless, at each iteration the computational complexity of the algorithms is still linear in the number of samples in the analysis window. Performance measures based on predictive distributions are used in simulation studies to compare the modelling and signal reconstruction performance of the proposed TVAR models to that of the standard fixed-parameter AR model on both synthetic and real speech data sets.
Bayesian separation and recovery of convolutively mixed autoregressive sources

In this paper we address the problem of the separation and recovery of convolutively mixed autoregressive processes in a Bayesian framework. Solving this problem requires the ability to solve integration and/or optimization problems of complicated posterior distributions. We thus propose efficient stochastic algorithms based on Markov chain Monte Carlo (MCMC) methods. We present three algorithms. The first one is a classical Gibbs sampler that generates samples from the posterior distribution. The two other algorithms are stochastic optimization algorithms that allow to optimize either the marginal distribution of the sources, or the marginal distribution of the parameters of the sources and mixing filters, conditional upon the observation. Simulations are presented.
Iterative algorithms for optimal state estimation of jump Markov linear systems

Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. We present three original deterministic and stochastic iterative algorithms for optimal state estimation of JMLS whose computational complexity at each iteration is linear in the data length. The first algorithm yields conditional mean estimates. The second algorithm is an algorithm that yields the marginal maximum a posteriori (MMAP) sequence estimate of the finite state Markov chain. The third algorithm is an algorithm that yields the MMAP sequence estimate of the continuous state of the JMLS. Convergence results for these three algorithms are obtained. Computer simulations are carried out to evaluate their performance.
Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC

In this paper, the problem of joint Bayesian model selection and parameter estimation for sinusoids in white Gaussian noise is addressed. An original Bayesian model is proposed that allows us to define a posterior distribution on the parameter space. All Bayesian inference is then based on this distribution. Unfortunately, a direct evaluation of this distribution and of its features, including posterior model probabilities, requires evaluation of some complicated high-dimensional integrals. We develop an efficient stochastic algorithm based on reversible jump Markov chain Monte Carlo methods to perform the Bayesian computation. A convergence result for this algorithm is established. In simulation, it appears that the performance of detection based on posterior model probabilities outperforms conventional detection schemes.
Sequential MCMC for Bayesian model selection

In this paper, we address the problem of sequential Bayesian model selection. This problem does not usually admit any closed-form analytical solution. We propose here an original sequential simulation-based method to solve the associated Bayesian computational problems. This method combines sequential importance sampling, a resampling procedure and reversible jump MCMC (Markov chain Monte Carlo) moves. We describe a generic algorithm and then apply it to the problem of sequential Bayesian model order estimation of autoregressive (AR) time series observed in additive noise.
Joint Bayesian detection and estimation of sinusoids embedded in noise

In this paper we address the problem of the joint detection and estimation of sinusoids embedded in noise, from a Bayesian point of view. We first propose an original Bayesian model. A large number of parameters has to be estimated, including the number of sinusoids. No analytical developments can be performed. This leads us to design a new stochastic algorithm relying on reversible jump MCMC (Markov chain Monte Carlo). We obtain very satisfactory results.
Bayesian filtering for hidden Markov models via Monte Carlo methods

We propose a new Monte Carlo method for Bayesian filtering of general nonlinear and non-Gaussian hidden Markov models. This method is an extension of the well known importance sampling method. It is especially well-suited to sequential simulation as it allows one to split or kill trajectories according to a given score function. The model and estimation objectives are described. The new Monte Carlo method is presented. A few results on this method are established and its application to Bayesian filtering is described. Simulation results for several nonlinear and non-Gaussian time series are presented.
Efficient simulated annealing algorithms for Bayesian parameter estimation

In this paper we present simple conditions related to geometric ergodicity of Markov chains which ensure the convergence in a given sense of the simulated annealing algorithm. We prove that convergence of the algorithm occurs for a proper sequence of temperatures when a local minorization condition of the transition kernels and a drift condition are satisfied. This result may be useful in a Bayesian framework, where it is possible to take advantage of the statistical structure of the problem in order to perform efficient optimization. This is illustrated on several examples.
Aluminium alloy corrosion detection by magnetic measurements

During corrosion, currents passing from anodes to cathodes generate magnetic fields. Pitting corrosion of aluminium alloys in aggressive media was detected through magnetic field measurements, without electrical connection to the sample and without reference or auxillary electrodes in the corrosion cell. A sodium chloride solution containing hydrogen peroxide or a solution of potassium hydroxide 2 mol l-1 solution was used for corrosion tests. Two magnetometers developed by LETI were used: a superconducting quantum interference device (SQUID) magnetometer and a nuclear magnetic resonance (NMR) magnetometer. The SQUID provided information on spatial distribution and direction of the currents. The NMR magnetometer was better for studying the development of the corrosion over time.
Efficient stochastic maximum a posteriori estimation for harmonic signals

In this paper, we address the problems of ML (Maximum Likelihood) parameter estimation and model order selection for harmonic signals using classical criteria. Solving these problems requires the maximization of complex multimodal functions. These optimization problems are shown as being equivalent to the estimation of joint and marginal maximum a posteriori (MAP) estimates under given Bayesian models. Efficient stochastic algorithms based on non-homogeneous Markov chain Monte Carlo methods are presented to solve these problems and their convergence is established. Computer simulations demonstrate the efficiency of these algorithms.
Blind marginal maximum a posteriori sequence estimation for finite impulse response channels

In this paper, we propose an original full Bayesian approach for blind equalization of discrete signals degraded by a finite impulse response (FIR) channel and observed in additive noise. The sequence of discrete symbols is estimated according to a marginal maximum a posteriori (MMAP) criterion, the other unknown parameters are regarded as random nuisance parameters and integrated out analytically. An efficient off-lime algorithm to maximize the marginal posterior distribution is proposed. Using Monte Carlo simulations, the performance of this algorithm is evaluated in terms of bit error rate (BER).
Robust Bayesian spectral analysis via MCMC sampling

In this paper, the harmonic retrieval problems in white Gaussian noise, non-Gaussian impulsive noise and in presence of threshold observations are addressed using a Bayesian approach. Bayesian models are proposed that allow us to define posterior distributions on the parameter space. All Bayesian inference is then based on these distributions. Unfortunately, a direct evaluation of these latters and of their features requires evaluation of some complicated high-dimensional integrals. Efficient stochastic algorithms based on Markov chain Monte Carlo methods are presented to perform Bayesian computation. In simulation, these algorithms are able to estimate the unknown parameters in highly degraded conditions.
Bayesian blind marginal separation of convolutively mixed discrete sources

We formulate the discrete source separation problem in a Bayesian framework. We show that it is possible to integrate analytically the so called nuisance parameters, leading to an analytic expression of the marginal posterior distribution of the symbols conditional upon the observations. We present two algorithms, a deterministic algorithm and stochastic algorithm, that allow one to optimize the marginal posterior distribution. We present simulation results and draw some conclusions.
Bayesian estimation of filtered point processes using Markov chain Monte Carlo methods

Filtered point processes model a huge amount of physical phenomena. Usually, only noisy observations are in practice available. From these data, one would like to estimate the parameters of the filtered point process. This is a complex problem which in general does not admit any closed-form solution. In this paper, we propose stochastic algorithms to perform statistical estimation for such processes in a Bayesian framework. These algorithms rely on Markov chain Monte Carlo methods which are powerful stochastic simulation methods.
Bayesian deconvolution of cyclostationary processes based on point processes

In this paper we address the problem of the Bayesian de-convolution of a widely spread class of processes, filtered point processes, whose underlying point process is a self-excited point process. In order to achieve this de-convolution, we perform powerful stochastic algorithm, the Markov chains Monte Carlo (MCMC), which despite their power have not been yet widely used in signal processing.
Measure of cyclostationarity for Gaussian processes based on the likelihood ratio test

The problem addressed in this paper is the detection of cyclostationarity, and the measurement of the trend of a process to have this property. This problem is of great importance, because in applications algorithms using the property of cyclostationarity assume the periodicities of the statistics to be known. Thus the periodicities need to be detected/estimated, and furthermore, a measure must be given in order to qualify the trend of a process to have a given periodicity. This measure will give information about the opportuneness of using a cyclostationary modelization instead of a stationary one.
Joint Bayesian model selection and blind equalization of ISI channels

We consider the problem of joint model selection and blind equalization of inter-symbol interference (ISI) channels. We adopt a Bayesian approach where nuisance parameters are considered random and integrated out. An efficient Markov chain Monte Carlo (MCMC) method is presented to perform Bayesian computation. The proposed algorithm overcomes the problem of delay ambiguity encountered by most existing MCMC algorithms. A simple Metropolis step is employed for model selection circumventing the need for reversible jump Markov chain Monte Carlo (RJMCMC). To the best of our knowledge, the problem of model selection in ISI channels is solved for the first time. The convergence behavior and the Bit Error Rate (BER) performance of our algorithm are demonstrated through computer simulations.
A particle filter to mitigate jamming for GPS navigation

This paper studies a deterministic particle filter for detecting and estimating jamming for GPS navigation. Jamming results in an sudden increase of the GPS measurement noise variance, hence a degradation of the positioning solution. The proposed methodology extends the state vector by including both the unknown noise variances and a latent process indicating variance jumps. A fixed lag smoothing algorithm makes the detection more robust to outliers. The sparseness of variance changes is advantageously taken into account in the proposed strategy
Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning

Sensor scheduling has been a topic of interest to the target tracking community for some years now. Recently, research into it has enjoyed fresh impetus with the current importance and popularity of applications in Sensor Networks and Robotics. The sensor scheduling problem can be formulated as a controlled Hidden Markov Model. In this paper, we address precisely this problem and consider the case in which the state, observation and action spaces are continuous valued vectors. This general case is important as it is the natural framework for many applications. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal
On the convergence of a two timescale stochastic optimisation algorithm for optimal observer trajectory planning

Sensor scheduling has been a topic of interest to the target tracking community for some years now and more recently, it has enjoyed fresh impetus with the current importance and popularity of applications in Sensor Networks and Robotics. The sensor scheduling problem can be formulated as a controlled Hidden Markov Model. In the paper , we addressed precisely this problem and considered the case in which the state, observation and action spaces are continuous. We presented a novel simulation-based method that used a two timescale stochastic approximation algorithm to find optimal actions. In this report, we study the convergence of the proposed stochastic approximation algorithm under general assumptions and for the specific observer trajectory planning application of [7].
Sequential Monte Carlo methods for multitarget filtering with random finite sets

Random finite sets (RFSs) are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for data fusion. Although the foundation has been established in the form of finite set statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multitarget filtering is not yet practical due to the inherent computational hurdle. Even the probability hypothesis density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multitarget posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multitarget filter. In addition, an SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving nonlinear nonGaussian dynamics. Convergence results for these filters are also established.
Particle Methods for Risk Sensitive Filtering

Risk sensitive filters (RSF) are known to be robust in the presence of uncertainties in the system parameters. Unfortunately these filters only admit closed form expressions for a very limited class of models including finite state-space Markov chains and linear Gaussian models. In this paper, we present an efficient Monte Carlo particle implementation of these filters for non-linear and non-Gaussian state-space models. This non-standard particle algorithm is based on a probabilistic interpretation of the RSF recursion. This algorithm significantly extends the range of applications of risk-sensitive techniques. Simulation results demonstrate the performance of the algorithm.
Particle methods for optimal filter derivative: application to parameter estimation

Particle filtering techniques are a popular set of simulation-based methods to perform optimal state estimation in nonlinear nonGaussian dynamic models. However, in applications related to control and identification, it is often necessary to be able to compute the derivative of the optimal filter with respect to parameters of the dynamic model. Several methods have already been proposed in the literature. In experiments, the approximation errors increase with the dataset length. We propose here original particle methods to approximate numerically the filter derivative. In simulations, these methods do not suffer from the problem mentioned. Applications to batch and recursive parameter estimation are presented.
Sequential auxiliary particle belief propagation

This paper discloses a novel algorithm for efficient inference in undirected graphical models using sequential Monte Carlo (SMC) based numerical approximation techniques. The methodology developed, titled "auxiliary particle belief propagation", extends the applicability of the much celebrated (Loopy) belief propagation (LBP) algorithm to non-linear, non-Gaussian models, whilst retaining a computational cost that is linear in the number of sample points (or particles). Furthermore, we provide an additional extension to this technique by analyzing temporally evolving graphical models, a problem which remains largely unexplored in the scientific literature. The work presented is thus a general framework that can be applied to a plethora of novel distributed fusion problems. In this paper, we apply our inference algorithm to the (sequential problem of) articulated object tracking.
Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches

This paper deals with model-based approaches for joint target tracking and identification. In a Bayesian framework, parametric state-space model classes are introduced as a generalization of the widespread state-space models. In addition to the dynamic state, they include a hyper-parameter, which takes into account target features or behaviors. For such model classes, sequential Monte Carlo approaches, also known as particle filtering, provide a powerful tool to perform sequentially on-line estimation and model selection. The paper focuses on the ergodicity concern of fixed hyper-parameter estimation and model selection. Indeed, the infinite memory of such a system may lead to the particle filter degeneracy or divergence. It reviews various methods to solve this problem, from the common and basic trick of adding an artificial noise to more complex methods, such as the introduction of reversible jump Markov chain Monte Carlo moves.