This paper presents new speech analysis method based on a Glottal-ARMAX (Auto Regressive and Moving Average eXogenous) model with phase compensation. A Glottal-ARMAX model consists of two kinds of inputs: glottal source model excitation and a white gauss input, and a vocal tract ARMAX model. The proposed method can simultaneously estimate the glottal source model and vocal tract ARMAX model par...

We have already developed a speech analysis method based on the Glottal-ARMAX (Auto Regressive and Moving Average eXogenous) model, in which the speech production model is supposed to be an ARMAX vocal tract model and two kinds of excitation: glottal source model excitation and white Gaussian. The speech analysis method based on the Glottal-ARMAX model can estimate the glottal source and ARMAX ...

Abstract: The problem of estimating a set of parameters in the autoregressive moving average model with exogenous inputs (ARMAX) is considered and a numerical Bayesian method proposed. This paper, develops a Bayesian analysis for the ARMAX model by implementing a fast, easy and accurate Gibbs sampling algorithm. The procedure is easy to implement and can be computed also when some priors in the...

This paper examines the identification of a single-output two-input system. Motivated by an experiment design problem (should one excite the two inputs simultaneously or separately), we examine the effect of the (second) input signal on the variance of the various polynomial coefficients in the case of FIR, ARX, ARMAX, OE and BJ models. A somewhat surprising result is to show that the addition ...

State-dependent parameter representations of stochastic non-linear sampled-data systems are studied. Velocity-based linearization is used to construct state-dependent parameter models which have a nominally linear structure but whose parameters can be characterized as functions of past outputs and inputs. For stochastic systems state-dependent parameter ARMAX (quasi-ARMAX) representations are o...

Purpose: This work aims to evaluate demand forecasting models determine if using exogenous factors and machine learning techniques helps improve performance compared univariate statistical models, allowing manufacturing companies manage better.Design/methodology/approach: We implemented a multivariate Auto-Regressive Moving Average with eXogenous input (ARMAX) model Neural Network-ARMAX (NN-ARM...

Vehicle crash test is considered to be the most direct and common approach to assess the vehicle crashworthiness. However, it suffers from the drawbacks of high experiment cost and huge time consumption. Therefore, the establishment of a mathematical model of vehicle crash which can simplify the analysis process is significantly attractive. In this paper, we present the application of LPV-ARMAX...

This paper presents an alternative approach to time series forecasting, through use of artificial neural networks (ANNs), a relatively new concept in hydrological research. Box and Jenkins ARMAX (autoregressive moving average with exogenous inputs) models have been widely used in modeling various time series with satisfactory results. This study shows that ANNs can produce comparable, to ARMAX,...