On computing the physical sources of jet noise

We derive a closed system of equations that relates the acoustically radiating flow variables to the sources of sound for homentropic flows. We use radiating density, momentum density and modified pressure as the dependent variables which leads to simple source terms for the momentum equations. The source terms involve the non-radiating parts of the density and momentum density fields. These non-radiating components are obtained by removing the radiating wavenumbers in the Fourier domain. We demonstrate the usefulness of this new technique on an axi-symmetric jet solution of the Navier-Stokes equations, obtained by direct numerical simulation (DNS). The dominant source term is proportional to the square of the non-radiating part of the axial momentum density. We compare the sound sources to that obtained by an acoustic analogy and find that they have more realistic physical properties. Their frequency content and amplitudes are consistent with. We validate the sources by computing the radiating sound field and comparing it to the DNS solution. © 2010 by S. Sinayoko, A. Agarwal.

Once again on the importance of propagation effects for jet noise modelling

Several turbulent jet noise models starting from the classical Lighthill acoustic analogy to state-of-the art models are considered. No attempt is made to present any complete overview of jet noise theories. Instead, the aim is to emphasise the importance of sound generation and meanflow effects for the understanding and prediction of jet noise. For a recent acoustic analogy model, the consequences of jet flow simplification on the predicted sound spectra shape and the effective noise source location in the jet are discussed. © 2010 by the American Institute of Aeronautics and Astronautics, Inc.

Extended VTS for noise-robust speech recognition

Model compensation is a standard way of improving the robustness of speech recognition systems to noise. A number of popular schemes are based on vector Taylor series (VTS) compensation, which uses a linear approximation to represent the influence of noise on the clean speech. To compensate the dynamic parameters, the continuous time approximation is often used. This approximation uses a point estimate of the gradient, which fails to take into account that dynamic coefficients are a function of a number of consecutive static coefficients. In this paper, the accuracy of dynamic parameter compensation is improved by representing the dynamic features as a linear transformation of a window of static features. A modified version of VTS compensation is applied to the distribution of the window of static features and, importantly, their correlations. These compensated distributions are then transformed to distributions over standard static and dynamic features. With this improved approximation, it is also possible to obtain full-covariance corrupted speech distributions. This addresses the correlation changes that occur in noise. The proposed scheme outperformed the standard VTS scheme by 10% to 20% relative on a range of tasks. © 2006 IEEE.

Parameter measurement for heavy-vehicle fuel consumption modelling

A mathematical model is developed to predict the energy consumption of a heavy vehicle. It includes the important factors of heavy-vehicle energy consumption, namely engine and drivetrain performances, losses due to accessories, aerodynamic drag, rolling resistance, road gradients, and driver behaviour. Novel low-cost testing methods were developed to determine engine and drivetrain characteristics. A simple drive cycle was used to validate the model. The model is able to predict the fuel use for a 371 tractor-semitrailer vehicle over a 4 km drive cycle within 1 per cent. This paper demonstrates that accurate and reliable vehicle benchmarking and model parameter measurement can be achieved without expensive equipment overheads, e.g. engine and chassis dynamometers.

Factor analysis based VTS and JUD noise estimation and compensation

Model based compensation schemes are a powerful approach for noise robust speech recognition. Recently there have been a number of investigations into adaptive training, and estimating the noise models used for model adaptation. This paper examines the use of EM-based schemes for both canonical models and noise estimation, including discriminative adaptive training. One issue that arises when estimating the noise model is a mismatch between the noise estimation approximation and final model compensation scheme. This paper proposes FA-style compensation where this mismatch is eliminated, though at the expense of a sensitivity to the initial noise estimates. EM-based discriminative adaptive training is evaluated on in-car and Aurora4 tasks. FA-style compensation is then evaluated in an incremental mode on the in-car task. © 2011 IEEE.

Speaker and noise factorisation on the AURORA4 task

For many realistic scenarios, there are multiple factors that affect the clean speech signal. In this work approaches to handling two such factors, speaker and background noise differences, simultaneously are described. A new adaptation scheme is proposed. Here the acoustic models are first adapted to the target speaker via an MLLR transform. This is followed by adaptation to the target noise environment via model-based vector Taylor series (VTS) compensation. These speaker and noise transforms are jointly estimated, using maximum likelihood. Experiments on the AURORA4 task demonstrate that this adaptation scheme provides improved performance over VTS-based noise adaptation. In addition, this framework enables the speech and noise to be factorised, allowing the speaker transform estimated in one noise condition to be successfully used in a different noise condition. © 2011 IEEE.