Statistical modelling and resynthesis of environmental texture sounds

TitleStatistical modelling and resynthesis of environmental texture sounds
Publication TypePhD Thesis
Year of Publication2015
UniversityUniversitat Pompeu Fabra
AuthorsKersten, S.
AdvisorPurwins, H., & Serra X.
Academic DepartmentDepartment of Information and Communication Technologies
Number of Pages171
AbstractEnvironmental texture sounds are an integral, though often overlooked, part of our daily life. They constitute those elements of our sounding environment that we tend to perceive subconsciously but which we miss when they are missing. Those sounds are also increasingly important for adding realism to virtual environments, from immersive artificial worlds through computer games to mobile augmented reality systems. This work spans the spectrum from data-driven stochastic sound synthesis methods to distributed virtual reality environments and their aesthetic and technological implications. We propose a framework for statistically modelling environmental texture sounds in different sparse signal representations. We explore three different instantiations of this framework, two of which constitute a novel way of representing texture sounds in a physically inspired sparse statistical model and of estimating model parameters from recorded sound examples. We propose a new method of creatively interacting with corpuses of sound segments that are organised in a two dimensional space and evaluate our work in a teaching context for musicians and sound artists. Finally, we describe two different authoring and simulation environments for creating sonic landscapes for virtual reality environments and augmented audio reality applications. These systems serve as a test bed for exploring possible applications of environmental texture sound synthesis models. We evaluate the validity of the developed systems in the context of a prototype virtual reality environment and within the commercial setting of a mobile location based audio platform. We also introduce a novel sound synthesis engine that serves as the basis for realtime rendering of large soundscapes. Its performance is evaluated in the context of a commercial location based audio platform that is in daily use by content producers and end users. In summary, this thesis contributes to the advancement of the state of the art in statistical modelling of environmental sound textures by exploring novel ways of representing those sounds in a sparse setting. Our research also significantly contributed to the succesfull realisation of an innovative location based audio platform.
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