This work is part of the Perceptually Optimised Sound Zone project (posz.org) which aims to develop sound zoning systems which reproduce audio programmes to multiple listening zones within automotive and domestic environments. This work describes the construction of a model to evaluate sound zoning systems. A framework for evaluating auditory interference scenarios is described in which either the target or interferer programme is masked, or where both programmes are audible and the listening scenario has some degree of acceptability. Masking and acceptability experiments were conducted to investigate the relationship between the two, and to determine boundaries of audibility. A linear correlation was found between masking and acceptability, and a linear regression model was constructed to predict thresholds of acceptability from masking thresholds. A masking threshold model was adapted and predictions were within 3 dB of the reported mean masking thresholds. Predictions of acceptability, using a linear regression and masking model combination, accounted for three quarters of the variance in acceptability. Further work focused on speech target programmes based on listener comments that the presence of speech affected acceptability. An experiment was conducted to gather intelligibility and acceptability data. Results showed that a high speech intelligibility marked the lower boundary of acceptability. Existing models for intelligibility prediction were evaluated and a time-windowed speech intelligibility index was shown to predict intelligibility with RMSE = 10.8%. Subsequently, a model was constructed to predict acceptability within these boundaries. Two experiments were conducted gathering training and validation data, and a training and selection procedure was carried out to methodically identify the most useful features. The selected model predictions had acceptability scores of RMSE = 11.1 - 17.9% across training and validation data. Finally, an algorithm was proposed for the prediction of acceptability in auditory interference scenarios. The algorithm consists of first predicting masking thresholds to determine the boundaries of acceptability. Then, for non-speech target programmes, the acceptability is predicted using a linear regression to the masking threshold; for speech target programmes, the intelligibility is calculated to revise the lower acceptability boundary and the speech acceptability model is used to predict acceptability.

Higher Order Ambisonics is a spatial audio technique that aims to recreate a sound image over as large a listening area as possible. Only limited investigation has taken place into localisation with Ambisonics and Higher Order Ambisonics at off-centre listening positions. This thesis presents the results of three psychoacoustic localisation experiments Investigating off-centre localisation of first and third order Ambisonics under different conditions: for studio and concert hall sized arrays, and for transient vs ongoing stimuli. A detailed analysis of the results of each experiment is carried out 'to determine the robustness of the tested systems. Comparisons are made between the results of the three experiments to determine the influence of changing the stimulus or increasing the arrival time delay between loudspeakers, where the relative gains of the loudspeakers was found to be perceptually more important than increased time differences between them. The usefulness of these results can be increased by comparison with models for prediction of human localisation, where a robust model would afford fast evaluation of ambisonic systems and allow system optimisation for off-centre positions. Therefore, evaluation Is performed for two binaural models on their ability to predict the results of the psychoacoustic experiments. A model by Dietz and a modified version of the Undemann model are evaluated. Finally, the energy vector model, prevalent in the Ambisonics community, is extended to include elements of the precedence effect. The binaural models, the standard energy vector, and the precedence-extended model are evaluated by comparison to the perceptual results. The robustness of each of the binaural and vector models is discussed in the context of their use as predictors of localisation at off-centre listening positions. The predictions of the precedence~extended energy vector are found to exhibit the lowest deviation from the perceptual results.

Techniques such as vibration monitoring, thermal analysis and oil analysis are well established as means to have been used to improve reliability of gearboxes and extend time-to-failure. In this area Acoustic Emission (AE) technology is still in its infancy but the attention shown by researchers towards this method has increased dramatically because several studies have shown the AE offers the important advantage of improved sensitivity over more conventional monitoring tools for the early detection and prediction of gear failure. Helical gear lubrication is critically important for maintaining the integrity of operating gears and the oil also prevents asperity contact at the gear mesh thereby protecting the gears from a deterioration process and surface failures. In gear systems, there are three types of lubrication regimes: Dry Running, Boundary Lubrication (BL), Hydrodynamic Lubrication (HL) and Elastohydro-dynamic Lubrication (EHL). The last regime is associated with the normal operating running condition of gears. Acoustic emissions were acquired from gears and analysed for different lubrication regimes (dry, BL, HL and EHL regimes at different temperatures), and corresponding specific film thicknesses (λ) levels. The results showed an inverse relationship between AE signal levels and specific film thickness (λ) of the oil. This relation was used to determine the lubrication regime from the measured AE signals. For instance, dry running had the highest AE levels which were attributed to the metal-to-metal contact of the gear mesh. The BL regime had relatively high AE levels which also attributed to the level of asperity contact is greater than the oil film thickness. The HL regime was characterized by the lowest AE levels due to the lubricant oil completely separating the teeth during gear meshing. Finally, the EHL regime showed intermediate AE levels compared to the BL and HL regimes because the oil film was less than for the HL regime but greater than for the BL regime. It is shown that the application of advanced signal processing methods is necessary for monitoring helical gears; Kurtosis and Spectral Kurtosis were used to investigate the AE signatures and found to be effective in de-noising (spectral kurtosis) acquired signals. Acoustic Emission proved to be a powerful tool to detect the oil regime for both defective and non-defective conditions. It is concluded that the experimental findings of this research programme will provide the foundations for significant advancement in the application of AE for the determining the lubrication regime present within a helical gearbox and for the detection of developing gear faults. This should give a new impetus in the field of maintenance and prevention of human and material catastrophes. Several papers presenting the findings of this research have been published in international journals and given at conferences.

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