Phase delay and group delay functions are well known in lumped system theory but they are not well understood, particularly in the context of non-minimum phase lumped systems. This paper presents four paradoxes that arise from an overly-literal interpretation of the phase delay function. The paradoxes are very simply illustrated using the example of a first order loudspeaker crossover. A new system response model is presented to resolve these paradoxes. The model is extended to higher order systems and to non-minimum phase systems. Applications and implications for system analysis are discussed.

Polarity (international relations)

Literal (mathematical logic)

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An intelligent ventilation system to attenuate the intermittent transportation noise

The Journal of the Acoustical Society of America (1997)

Ramin Mohajeri John D. Atkinson Fergus R. Fricke

The level of intermittent transportation noise into the bulding is controlled by activating an intelligent control system to close the window when the noise occurs. This method is a possible solution for conflict between passive noise control and natural ventilation for places with a moderate climate. A knowledge-based control strategy has been implemented to control the position of the window using acoustic feedback. There are three major steps, training, source classification, and control, in which fuzzy logic and rule-based algorithms are implemented. If somebody plays music or speaks near the window, the intelligent window must not react even though the level of the sound may be higher than an approaching aircraft or vehicle. To identify an approaching noise source, centroid of the Wigner–Ville distribution and the peaks of STFT are used. The cross correlation between output data from indoor and outdoor microphones indicates the effect of outdoor transportation noise on the indoor noise level. A training software is considered to update the thresholds of noise classification. The experimental result of a prototype intelligent window is presented. A noise attenuation of more than 10 dB in Leq and Lmax in each single noise events is achieved. [Work supported by RTA of NSW.]

Ambient noise level

Environmental Noise

10.1121/1.418917

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The Prediction of Reverberation Time Using Optimal Neural Networks

Building Acoustics (2002)

Joseph Nannariello Fergus R. Fricke

A neural network approach to predicting the reverberation time, RT 60 , at the conceptual design stage of auditoria, and churches is presented. The results of investigations previously carried out indicated that there was a good basis for using trained neural networks to predict the reverberation time for unoccupied enclosures but that 15 input variables were required to achieve the desired accuracy. As the number of input variables that can be readily identified and quantified at the early design stage is small, the objective of this work is to reduce network size and to obtain optimal neural networks. The results showed that the generalization performance of neural networks with simplified internal representation is efficient. Generally, the reverberation time prediction accuracy of the network models, for the six enclosures ‘tested’, is within the range of the subjective difference limen (ΔT/T ≈ 5%).

Representation

10.1260/135101002761035717

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Noise inside buildings due to external air flows

Applied Acoustics (1972)

Fergus R. Fricke

Air movement

Lift (data mining)

10.1016/0003-682x(72)90027-8

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