Speech enhancement based on AR model parameters estimation

2016 ◽  
Vol 79 ◽  
pp. 30-46 ◽  
Author(s):  
Feng Deng ◽  
Changchun Bao
Keyword(s):  
Speech Enhancement ◽  
Parameters Estimation ◽  
Ar Model ◽  
Model Parameters

A Space Time Autoaggressive Method Based on Parameters Estimation for Airborne MIMO Radar

2014 ◽  
Vol 556-562 ◽  
pp. 4496-4500
Author(s):  
Xing Hui Chen ◽  
Shi Qiao Gao
Keyword(s):  
Estimation Method ◽  
Mimo Radar ◽  
Space Time ◽  
Parameters Estimation ◽  
Ar Model ◽  
Model Parameters ◽  
Clutter Suppression ◽  
Training Samples ◽  
Multiple Input ◽  
Space Time Adaptive Processing

The clutter distribution of an airborne multiple input and multiple output (MIMO) radar in non-homogeneous environment varies with ranges and samples in different range gates are not independent identically distributed vectors, so that the statistical space time adaptive processing (STAP) methods degrade heavily. A clutter suppression method for airborne MIMO radar in non-homogeneous environments is studied in this paper. Firstly, Space time autoaggressive (STAR) method is introduced to airborne MIMO radar for clutter suppression and then an AR model parameters estimation method for STAR is proposed to decrease the complexity of traditional method. Simulation results show the proposed method can estimate parameters exactly and rapidly with only few training samples and be fit for clutter suppression in non-homogeneous environments.

scholarly journals Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement

Signals ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 434-455
Author(s):  
Sujan Kumar Roy ◽  
Kuldip K. Paliwal
Keyword(s):  
Kalman Filter ◽  
Speech Enhancement ◽  
Linear Prediction ◽  
Real Life ◽  
Model Parameters ◽  
Noise Variance ◽  
Noisy Speech ◽  
Kalman Gain ◽  
Whitening Filter ◽  
Prediction Coefficient

Inaccurate estimates of the linear prediction coefficient (LPC) and noise variance introduce bias in Kalman filter (KF) gain and degrade speech enhancement performance. The existing methods propose a tuning of the biased Kalman gain, particularly in stationary noise conditions. This paper introduces a tuning of the KF gain for speech enhancement in real-life noise conditions. First, we estimate noise from each noisy speech frame using a speech presence probability (SPP) method to compute the noise variance. Then, we construct a whitening filter (with its coefficients computed from the estimated noise) to pre-whiten each noisy speech frame prior to computing the speech LPC parameters. We then construct the KF with the estimated parameters, where the robustness metric offsets the bias in KF gain during speech absence of noisy speech to that of the sensitivity metric during speech presence to achieve better noise reduction. The noise variance and the speech model parameters are adopted as a speech activity detector. The reduced-biased Kalman gain enables the KF to minimize the noise effect significantly, yielding the enhanced speech. Objective and subjective scores on the NOIZEUS corpus demonstrate that the enhanced speech produced by the proposed method exhibits higher quality and intelligibility than some benchmark methods.

A COMPARATIVE STUDY OF PHENOMENOLOGICAL MODELS OF MR BRAKE BASED ON NEURAL NETWORKS APPROACH

2013 ◽  
Vol 11 (01) ◽  
pp. 1350009 ◽  
Author(s):  
WITOLD PAWLUS ◽  
HAMID REZA KARIMI
Keyword(s):  
Neural Networks ◽  
Vibration Suppression ◽  
Feedforward Neural Networks ◽  
Active Suspension ◽  
Full Scale ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Systems Identification ◽  
Active Vibration ◽  
Models Validation

In this paper a full-scale commercially available magnetorheological (MR) brake installed in a semi-active suspension (SAS) system is modeled and simulated. Two well-known phenomenological hysteresis models are explored: Bouc–Wen and Dahl ones. In particular, influence of their parameters on the response is evaluated and assessed. The next step is to introduce the artificial neural networks and discuss their application in the field of systems identification. Subsequently, two feedforward neural networks are created and trained to estimate parameters characterizing each of the MR damper models described. The semi-active suspension (SAS) system equipped with a MR brake is described and the detailed procedure for acquisition of the reference data used in the models validation stage is elaborated. The models outputs obtained by simulating them with the values of coefficients as identified by the networks are compared to each other as well as to the reference experimental data. Thanks to that, the comparative analysis between the suggested vibration suppression models and the full-scale MR brake is done and it is concluded which of the discussed models has a better performance. The usability of neural networks in the field of parameters estimation of the mathematical models of the real world phenomena is described as well. The novelty of the presented methodology is the application of artificial intelligence methods to estimate model parameters of a MR brake utilized in a SAS system. The results of this approach have a strong potential to be successfully implemented in the area of model-based control of semi-active vibration suppression systems.

scholarly journals Estimation of baseflow parameters of variable infiltration capacity model with soil and topography properties for predictions in ungauged basins

2011 ◽  
Vol 8 (4) ◽  
pp. 7017-7053 ◽  
Author(s):  
Z. Bao ◽  
J. Liu ◽  
J. Zhang ◽  
G. Fu ◽  
G. Wang ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Model Simulation ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Ungauged Catchments ◽  
Vic Model ◽  
Predictions In Ungauged Basins

Abstract. Equifinality is unavoidable when transferring model parameters from gauged catchments to ungauged catchments for predictions in ungauged basins (PUB). A framework for estimating the three baseflow parameters of variable infiltration capacity (VIC) model, directly with soil and topography properties is presented. When the new parameters setting methodology is used, the number of parameters needing to be calibrated is reduced from six to three, that leads to a decrease of equifinality and uncertainty. This is validated by Monte Carlo simulations in 24 hydro-climatic catchments in China. Using the new parameters estimation approach, model parameters become more sensitive and the extent of parameters space will be smaller when a threshold of goodness-of-fit is given. That means the parameters uncertainty is reduced with the new parameters setting methodology. In addition, the uncertainty of model simulation is estimated by the generalised likelihood uncertainty estimation (GLUE) methodology. The results indicate that the uncertainty of streamflow simulations, i.e., confidence interval, is lower with the new parameters estimation methodology compared to that used by original calibration methodology. The new baseflow parameters estimation framework could be applied in VIC model and other appropriate models for PUB.

Condition monitoring based on modified CUSUM and EWMA control charts

2018 ◽  
Vol 24 (1) ◽  
pp. 119-132 ◽  
Author(s):  
Suzana Paula Gomes Fernando da Silva Lampreia ◽  
José Fernando Gomes Requeijo ◽  
José António Mendonça Dias ◽  
Valter Martins Vairinhos ◽  
Patrícia Isabel Soares Barbosa
Keyword(s):  
Control Charts ◽  
Moving Average ◽  
Failure Detection ◽  
Parameters Estimation ◽  
False Alarms ◽  
Model Parameters ◽  
Content Type ◽  
Catastrophic Failures ◽  
The Right ◽  
Weighted Moving Average

Purpose The application of condition-based maintenance on selected equipment can allow online monitoring using fixed, half-fixed or portable sensors. The collected data not always allow a straightforward interpretation and many false alarms can happen. The paper aims to discuss these issues. Design/methodology/approach Statistical techniques can be used to perform early failure detection. With the application of Cumulative Sum (CUSUM) Modified Charts and the Exponentially Weighted Moving Average (EWMA) Charts, special causes of variation can be detected online and during the equipment functioning. Before applying these methods, it is important to check data for independence. When the independence condition is not verified, data should be modeled with an ARIMA (p, d, q) model. Parameters estimation is obtained using the Shewhart Traditional Charts. Findings With data monitoring and statistical methods, it is possible to detect any system or equipment failure trend, so that we can act at the right time to avoid catastrophic failures. Originality/value In this work, an electro pump condition is monitored. Through this process, an anomaly and four stages of aggravation are forced, and the CUSUM and EWMA modified control charts are applied to test an online equipment monitoring. When the detection occurs, the methodology will have rules to define the degree of intervention.

scholarly journals Analytical Approximations of Long-Exposure Point Spread Functions and their Use

1989 ◽  
Vol 8 ◽  
pp. 657-661
Author(s):  
O. Bendinelli ◽  
G. Parmeggiani ◽  
F. Zavatti
Keyword(s):  
Gaussian Model ◽  
Parameters Estimation ◽  
Model Parameters ◽  
Point Spread Functions ◽  
Spherical Source ◽  
Analytical Approximations ◽  
Correct Model ◽  
Point Spread ◽  
Surface Image ◽  
Star Images

AbstractThe observed light distribution in long exposure star images (PSF) may be fitted equally well by a variety of models. But dealing with undersainpled star images, only the use of the multi-Gaussian model allows the correct model parameters estimation, taking into account integration on pixel surface, image off-centering and background behaviour. It is also shown that the convolution of a spherical source with the multi-Gaussian and Moffat’s models gives in practice the same result.

scholarly journals Online Parameter Identification of the Lithium-Ion Battery with Refined Instrumental Variable Estimation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
An Wen ◽  
Jinhao Meng ◽  
Jichang Peng ◽  
Lei Cai ◽  
Qian Xiao
Keyword(s):  
Instrumental Variable ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Parameters Estimation ◽  
Measurement Noise ◽  
Recursive Least Squares ◽  
Ar Model ◽  
Noise Model ◽  
Li Ion Battery ◽  
Li Ion

Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.

scholarly journals Regional Parameter Estimation of the SWAT Model: Methodology and Application to River Basins in the Peruvian Pacific Drainage

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3198
Author(s):  
Flavio Alexander Asurza-Véliz ◽  
Waldo Sven Lavado-Casimiro
Keyword(s):  
Water Balance ◽  
Assessment Tool ◽  
Hydrological Cycle ◽  
Swat Model ◽  
Parameters Estimation ◽  
Data Availability ◽  
Model Parameters ◽  
Hydrological Data ◽  
The Pacific ◽  
Main Components

This study presents a methodology for the regional parameters estimation of the SWAT (Soil and Water Assessment Tool) model, with the objective of estimating daily flow series in the Pacific drainage under the context of limited hydrological data availability. This methodology has been designed to obtain the model parameters from a limited number of basins (14) to finally regionalize them to basins without hydrological data based on physical-climatic characteristics. In addition, the bootstrapping method was selected to estimate the uncertainty associated with the parameters set selection in the regionalization process. In general, the regionalized parameters reduce the initial underestimation which is reflected in a better quantification of daily flows, and improve the low flows performance. Furthermore, the results show that the SWAT model correctly represents the water balance and seasonality of the hydrological cycle main components. However, the model does not correctly quantify the high flows rates during wet periods. These findings provide supporting information for studies of water balance and water management on the Peruvian Pacific drainage. The approach and methods developed can be replicated in any other region of Peru.

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