Spatial aspects of auditory salience

Models of auditory salience aim to predict which sounds attract people’s attention, and their proposed applications range from soundscape design to machine listening systems and object-based broadcasting. A few different types of models have been proposed, but one of the areas where most of them still fall short is spatial aspects of sound – they usually operate on mono signals and do not consider spatial auditory scenes. Part of the reason why this is the case might be that the relationship between auditory salience and position of sound is still not clear. In addition, methods used to measure auditory salience vary greatly, and authors in the field do not always use the same definition of salience.In Part I, this thesis aims to answer questions about the effect of spatial location of sound on auditory salience. This is done in four different experiments, which are based on previously published experimental methods but adapted to measure spatial effects. In general, the combined results of these experiments do not support the hypothesis that the spatial position of a sound alone influences how salient the sound is. However, they do show that unexpectedchanges in position might activate the deviance detection mechanism and therefore be salient. In addition, an experiment comparing three of the methods used reveals at least twodimensions of salience, which are measured by different methods to different extent. This emphasises the importance of carefully considering which experimental methods are used tomeasure auditory salience, and also providing a clear definition of what type of salience is of interest.Part II demonstrates how spatial position of sound can be incorporated into an auditory salience model. The results of experiments described in this thesis support the idea that the basis of auditory salience is the violation of expectations. The surprise caused by a sudden change in sound position can therefore be modelled by a Kalman-filter-based deviance detection model, which predicts experimental data discussed above with good accuracy.Finally, an example is given of how an application of such a model can improve the performance of a machine learning algorithm for acoustic event detection.

Level dc:type.qualificationlevel

Doctoral (Level 8)

Year dc:date.issued

2020

Author dc:creator

• Podwinska, Z

Language dc:language

en

Identifier

oai:salford-repository.worktribe.com:1344819

OAI identifier oai:identifier

oai:salford-repository.worktribe.com:1344819