CACSD, GUI's, and the multivariable frequency domain toolbox

This talk describes a new version of the Multivariable Frequency Domain Toolbox for Matlab. The intellectual issue which arises here is whether there is a role for Matlab-4 GUI facilities in a Toolbox which provides relatively low-level functionality, with a correspondingly random pattern of user interaction. My belief is that there is a role, but it is very restricted: in effect only for providing convenient 'viewing' facilities for low-level objects (which are multivariable frequency responses in the case of the MFD Toolbox). There is a more obvious role for a GUI with higher-level functions, such as frequency domain identification or parametric controller optimisation.

An efficient frequency-domain algorithm for wave scattering problems with application to jet noise

This paper presents a pseudo-time-step method to calculate a (vector) Green function for the adjoint linearised Euler equations as a scattering problem in the frequency domain, for use as a jet-noise propagation prediction tool. A method of selecting the acoustics-related solution in a truncated spatial domain while suppressing any possible shear-layer-type instability is presented. Numerical tests for 3-D axisymmetrical parallel mean flows against semi-analytical reference solutions indicate that the new iterative algorithm is capable of producing accurate solutions with modest computational requirements.

Adjoint linearised Euler solver in the frequency domain for jet noise modelling

The paper is devoted to extending the new efficient frequency-domain method of adjoint Green's function calculation to curvilinear multi-block RANS domains for middle and farfield sound computations. Numerical details of the method such as grids, boundary conditions and convergence acceleration are discussed. Two acoustic source models are considered in conjunction with the method and acoustic modelling results are presented for a benchmark low-Reynolds-number jet case.

Bayesian estimation of simultaneous musical notes based on frequency domain modelling

This paper proposes a Bayesian method for polyphonic music description. The method first divides an input audio signal into a series of sections called snapshots, and then estimates parameters such as fundamental frequencies and amplitudes of the notes contained in each snapshot. The parameter estimation process is based on a frequency domain modelling and Gibbs sampling. Experimental results obtained from audio signals of test note patterns are encouraging; the accuracy is better than 80% for the estimation of fundamental frequencies in terms of semitones and instrument names when the number of simultaneous notes is two.