Real-Time Vocal Tract Modelling

To date, most speech synthesis techniques have relied upon the representation of the vocal tract by some form of filter, a typical example being linear predictive coding (LPC). This paper describes the development of a physiologically realistic model of the vocal tract using the well-established technique of transmission line modelling (TLM). This technique is based on the principle of wave scattering at transmission line segment boundaries and may be used in one, two, or three dimensions. This work uses this technique to model the vocal tract using a one-dimensional transmission line. A six-port scattering node is applied in the region separating the pharyngeal, oral, and the nasal parts of the vocal tract

Discrete-Time Interest Rate Modelling

This paper presents an axiomatic scheme for interest rate models in discrete time. We take a pricing kernel approach, which builds in the arbitrage-free property and provides a link to equilibrium economics. We require that the pricing kernel be consistent with a pair of axioms, one giving the inter-temporal relations for dividend-paying assets, and the other ensuring the existence of a money-market asset. We show that the existence of a positive-return asset implies the existence of a previsible money-market account. A general expression for the price process of a limited-liability asset is derived. This expression includes two terms, one being the discounted risk-adjusted value of the dividend stream, the other characterising retained earnings. The vanishing of the latter is given by a transversality condition. We show (under the assumed axioms) that, in the case of a limited-liability asset with no permanently-retained earnings, the price process is given by the ratio of a pair of potentials. Explicit examples of discrete-time models are provided

Time-varying parametric modelling and time-dependent spectral characterisation with applications to EEG signals using multi-wavelets

A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The time-varying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method

Modelling Stabilometric Time Series

Stabilometry is a branch of medicine that studies balance-related human functions. Stabilometric systems generate time series. The analysis of these time series using data mining techniques can be very useful for domain experts. In the field of stabilometry, as in many other domains, the key nuggets of information in a time series are concentrated within definite time periods known as events. In this paper, we propose a technique for creating reference models for stabilometric time series based on event analysis. After testing the technique on time series recorded by top-competition sportspeople, we conclude that stabilometric models can be used to classify individuals by their balance-related abilitie

TIME Headway Modelling of Motorcycle-Dominated Traffic to Analyse Traffic Safety Performance and Road Link Capacity of Single Carriageways

This study aims to develop time headway distribution models to analyse traffic safety performance and road link capacities for motorcycle-dominated traffic in Denpasar, Bali. Three road links selected as the case study are Jl. Hayam Wuruk, Jl.Hang Tuah, and Jl. Padma. Data analysis showed that between 55%-80% of motorists in Denpasar during morning and evening peak hours paid less attention to the safe distance with the vehicles in front. The study found that Lognormal distribution models are best to fit time headway data during morning peak hours while either Weibull (3P) or Pearson III distributions is for evening peak hours. Road link capacities for mixed traffic predominantly motorcycles are apparently affected by the behaviour of motorists in keeping safe distance with the vehicles in front. Theoretical road link capacities for Jl. Hayam Wuruk, Jl. Hang Tuah and Jl. Padma are 3,186 vehicles/hour, 3,077 vehicles/hour and 1935 vehicles/hour respectively

Graphical modelling of multivariate time series

We introduce graphical time series models for the analysis of dynamic relationships among variables in multivariate time series. The modelling approach is based on the notion of strong Granger causality and can be applied to time series with non-linear dependencies. The models are derived from ordinary time series models by imposing constraints that are encoded by mixed graphs. In these graphs each component series is represented by a single vertex and directed edges indicate possible Granger-causal relationships between variables while undirected edges are used to map the contemporaneous dependence structure. We introduce various notions of Granger-causal Markov properties and discuss the relationships among them and to other Markov properties that can be applied in this context.Comment: 33 pages, 7 figures, to appear in Probability Theory and Related Field