The study of urban soundscapes has gained momentum in recent years as more people become concerned with the level of noise around them and the negative impact this can have on comfort. Monitoring the sounds present in a sonic environment can be a laborious and time–consuming process if performed manually. Therefore, techniques for automated signal identification are gaining importance if soundscapes are to be objectively monitored. This thesis presents a novel approach to feature extraction for the purpose of classifying urban audio events, adding to the library of techniques already established in the field. The research explores how techniques with their origins in the encoding of speech signals can be adapted to represent the complex everyday sounds all around us to allow accurate classification. The analysis methods developed herein are based on the zero–crossings information contained within a signal. Originally developed for the classification of bioacoustic signals, the codebook of Time–Domain Signal Coding (TDSC) has its band–limited restrictions removed to become more generic. Classification using features extracted with the new codebook achieves accuracies of over 80% when combined with a Multilayer Perceptron classifier. Further advancements are made to the standard TDSC algorithm, drawing inspiration from wavelets, resulting in a novel dyadic representation of time–domain features. Carrying the label of Multiscale TDSC (MTDSC), classification accuracies of 70% are achieved using these features. Recommendations for further work focus on expanding the library of training data to improve the accuracy of the classification system. Further research into classifier design is also suggested.

Denoising is an essential ingredient of any data processing task because real data are usually contaminated by some amount of uncertainty, error or noise. The ultimate objective in this study is to handle the multiresolution denoising of an arbitrarily spaced multidimensional data set contaminated with arbitrary noise. Denoising is closely related to function estimation from noisy samples, which is best achieved by complexity control in a structured function space. Multiresolution analysis and wavelets provide a suitable structured space for function estimation. However, conventional wavelet decompositions, such as the fast wavelet transform, are designed for regularly spaced data. Furthermore, the projection and lifting scheme approaches for dealing with irregular data cannot be easily extended to higher dimensions and their application to denoising is not straightforward. In contrast, the least squares wavelet decomposition offers a method for direct decomposition and denoising of multidimensional irregularly spaced data. We show that the frequently applied level by level multiresolution least squares wavelet decomposition suffers from gross interpolation error in the case of irregularly spaced data. The simultaneous least squares wavelet decomposition, with careful wavelet selection, is proposed to overcome this problem. Conventional wavelet domain denoising techniques, such as global and level dependent thresholding, work well for regularly spaced data but more sophisticated coefficient dependent thresholding is required for irregularly spaced data. We propose a new data domain denoising method for Gaussian noise, referred to as the Local Goodness of Fit (LGF) algorithm, which is based on the local application of the conventional goodness of fit measure in a multiresolution structure. We show that the combination of the simultaneous least squares wavelet decomposition and the LGF denoising algorithm is superior to the projection and coefficient dependent thresholding and can handle arbitrarily spaced multidimensional data contaminated with independent, but not necessarily identically distributed, Gaussian noise. For denoising of data contaminated with outliers and/or non-Gaussian long tail noise, the decomposition methods based on mean estimation are not robust. We develop a new robust multiresolution decomposition, based on median estimation in a dyadic multiresolution structure, referred to as the Interpolated Block Median Decomposition (IBMD). The IBMD method overcomes the limitations of existing median preserving transforms and can handle multidimensional irregularly spaced data of arbitrary size. Thresholding methods for the coefficients of robust median preserving decompositions are currently limited to regular data contaminated with noise drawn independently and identically from a known symmetric distribution. To overcome these serious limitations, we develop a fundamentally new data domain robust multiresolution denoising procedure, called the Local Balance of Fit (LBF) algorithm, which is based on local balancing of the data points above and below the denoised function in a dyadic multiresolution structure. The LBF algorithm, which was inspired by the intuitive denoising style carried out by a human operator, is a distribution free method that can handle any arbitrary noise without a priori knowledge or estimation of the noise distribution. The combination of the robust IBMD decomposition and the LBF denoising algorithm can effectively handle a wide spectrum of denoising applications involving multidimensional arbitrarily spaced data contaminated with arbitrary and unknown noise. The only limitation is that the noise samples must be independent or uncorrelated.

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The general aim of this thesis is to investigate the suitability of a stochastic method for computational aeroacoustics, the specific objective being to devise a stochastic method to generate synthetic turbulence and combine it with the linearised Euler equations to predict broadband fan interaction noise. In modern turbofan designs broadband fan noise is a dominant source of aircraft noise, the most efficient source being the interaction between upstream turbulence and the stator vanes. The stochastic method developed to generate synthetic turbulence reproduces twodimensional isotropic turbulent flows by filtering a random field. The fillter is expressed in terms of the energy spectrum and controls the spatial properties of the synthetic turbulence. In contrast with previous work, non-Gaussian filters are developed to model more realistic energy spectra such as Liepmann and von Karman spectra. The temporal decorrelation present in turbulent flows is modelled using Langevin Equations. A standard Langevin equation and a second-order Langevin model are derived in details and validated for fan interaction noise. In contrast with classical methods to generate synthetic turbulence, random-vortex-particle methods can be extended to cope with inhomogeneous non-stationary turbulence with little modification from the formulation for homogeneous turbulence. The stochastic method is applied for first time to broadband fan interaction noise. The method is firstly validated for frozen turbulence interacting with an airfoil. The temporal decorrelation is then included in the method to assess the influence of the integral time scale on the radiated acoustic sound field. The method is also combined with an existing wake model to represent the inhomogeneous non-stationary turbulent flow found downstream of a fan. Finally, comparison with existing experimental data for an isolated airfoil in a turbulent jet demonstrates the benefits of using more realistic energy spectra

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This thesis is concerned with semi-active damping control for vibration isolation of base disturbances. The aim is to investigate the effectiveness and suitability of semi-active damping control strategies for improving steady-state vibration isolation. A single-degree-of-freedom (SDOF) system, comprising a semi-active damper with a linear passive spring in parallel, is used to study the vibration isolation of base excitation. The semi-active control strategies investigated include on-off skyhook control, continuous skyhook control, on-off balance control and continuous balance control. Chatter and jerk problems are investigated, which can arise in numerical simulations and possibly in practice when using semi-active control strategies. Anti-chatter and anti-jerk control strategies are proposed. These control strategies are implemented numerically in Matlab/Simulink. Harmonic, periodic and random disturbances are considered in this thesis. The vibration isolation performance is evaluated in terms of Root-Mean-Square (RMS) acceleration transmissibility. The performance of these control strategies for the isolation of harmonic disturbances is firstly studied. The performance is compared with those of an adaptive-passive control strategy, a conventional and a skyhook passive damper. Results show that the semi-active control strategies can provide a better isolation than a conventional passive system with an equivalent damping level. The semi-active damper can provide isolation over the whole frequency range if the on-state damping of the semi-active damper is big enough. The fraction of time when the damper is turned on or off is found to be frequency dependent. The effects of secondary frequency, which is a harmonic or subharmonic of the fundamental frequency on switching time of the semi-active damper for isolation of the primary harmonic are examined. Upper bounds are derived for fraction of time when the switching time for the fundamental frequency may be affected by the presence of a secondary frequency. The performance of the semi-active isolation system for periodic and random disturbances, where there is more than one harmonic in the disturbance spectrum is investigated. The results for square wave and triangular wave disturbances suggest that semi-active control strategies are promising for periodic disturbance. Three special cases are considered for random disturbances when the acceleration, velocity and displacement inputs have flat spectra. The semi-active control strategies can provide some advantage in performance for random velocity and displacement disturbances with medium to high damping ratios. Only continuous skyhook control strategy can provide some benefit in isolation performance for random acceleration disturbances. Following on from the numerical simulations, experimental work is carried out to validate the simulation results. The experimental set-up incorporates an electromagnetic device as a semi-active damper. The on-off skyhook control algorithm is chosen to be implemented using an analogue circuit. The damping of the electromagnetic semi-active damper is achieved by opening and closing the magnet-coil circuit. Numerical predictions are confirmed by experimental observation. The performance of the electromagnetic damper is limited by the achievable damping level.

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