Time-invariant Model Parameters Research Articles

HMM-Based Gain Modeling for Enhancement of Speech in Noise

Accurate modeling and estimation of speech and noise gains facilitate good performance of speech enhancement methods using data-driven prior models. In this paper, we propose a hidden Markov model (HMM)-based speech enhancement method using explicit gain modeling. Through the introduction of stochastic gain variables, energy variation in both speech and noise is explicitly modeled in a unified framework. The speech gain models the energy variations of the speech phones, typically due to differences in pronunciation and/or different vocalizations of individual speakers. The noise gain helps to improve the tracking of the time-varying energy of nonstationary noise. The expectation-maximization (EM) algorithm is used to perform offline estimation of the time-invariant model parameters. The time-varying model parameters are estimated online using the recursive EM algorithm. The proposed gain modeling techniques are applied to a novel Bayesian speech estimator, and the performance of the proposed enhancement method is evaluated through objective and subjective tests. The experimental results confirm the advantage of explicit gain modeling, particularly for nonstationary noise sources

Optimal adaptive control of linear systems with unknown measurement subsystems

For the quadratic cost, adaptive stochastic control problem with linear plant and measurement models excited by white Gaussian noise, and with unknown time invariant model parameters in the measurement subsystem, the optimal stochastic control is obtained. It is shown that separation between estimation and control holds in this case. The controller is shown to be linear and deterministic, while the estimator is shown to consist of a bank of known nonlinear functions of model conditional, causal, mean-square state-vector estimates. The estimator can again be partitioned into a bank of linear, model conditional, optimal, nonadaptive estimates, one for each admissible value of unknown parameter vector θ, and a nonlinear bank of a posteriori model probabilities which incorporate the adaptive nature of the optimal adaptive control. The results are presented both for continuous-time and discrete-time systems. A special case of the above problem is also discussed. It is the case of joint detection and control.

Optimal adaptive control: A non-linear separation theorem†

Abstract For the quadratic cost, non-linear, adaptive stochastic control problem with linear plant and measurement models excited by white gaussian noise, and unknown time-invariant model parameters, the optimal stochastic control is obtained and shown to separate (‘non-linear separation theorem’) into a bank of model-conditional deterministic controller gains and a corresponding bank of known non-linear functional of the model-conditional, causal, mean-square state-vector estimates. This separation may also be viewed as a decomposition of the optimal, non-linear adaptive control into a bank of model-conditional, optimal, non-adaptive linear controls, one for each admissible value fof the unknown parameter θ and a nonlinear part, namely the bank of a posteriori model probabilities, which incorporate the adaptive nature, of the optimal adaptive control. Results are given for a special case of the above problem— namely, uncertainty in the measurement matrix—that exhibit drastically reduced computational req...