Search Content

Matching Items (3)

Filtering by

Creators: Kosut, Oliver
Creators: School of Humanities, Arts, and Cultural Studies
Member of: Barrett, The Honors College Thesis/Creative Project Collection
Resource Type: Text

SoundSwarm: An Interactive Exploration of 3-Dimensional and Behavioral Modeled Sound

Description

This paper outlines the development of a software application that explores the plausibility and potential of interacting with three-dimensional sound sources within a virtual environment. The intention of the software application is to allow a user to become engaged with a collection of sound sources that can be perceived both graphically and audibly within a spatial, three-dimensional context. The three-dimensional sound perception is driven primarily by a binaural implementation of a higher order ambisonics framework while graphics and other data are processed by openFrameworks, an interactive media framework for C++. Within the application, sound sources have been given behavioral functions such as flocking or orbit patterns, animating their positions within the environment. The author will summarize the design process and rationale for creating such a system and the chosen approach to implement the software application. The paper will also provide background approaches to spatial audio, gesture and virtual reality embodiment, and future possibilities for the existing project.

ContributorsBurnett, Garrett (Author) / Paine, Garth (Thesis director) / Pavlic, Theodore (Committee member) / School of Humanities, Arts, and Cultural Studies (Contributor) / School of Arts, Media and Engineering (Contributor) / Barrett, The Honors College (Contributor)

Created2016-05

Maximum Entropy Surrogation in Multiple Channel Signal Detection

Description

Multiple-channel detection is considered in the context of a sensor network where data can be exchanged directly between sensor nodes that share a common edge in the network graph. Optimal statistical tests used for signal source detection with multiple noisy sensors, such as the Generalized Coherence (GC) estimate, use pairwise measurements from every pair of sensors in the network and are thus only applicable when the network graph is completely connected, or when data are accumulated at a common fusion center. This thesis presents and exploits a new method that uses maximum-entropy techniques to estimate measurements between pairs of sensors that are not in direct communication, thereby enabling the use of the GC estimate in incompletely connected sensor networks. The research in this thesis culminates in a main conjecture supported by statistical tests regarding the topology of the incomplete network graphs.

ContributorsCrider, Lauren Nicole (Author) / Cochran, Douglas (Thesis director) / Renaut, Rosemary (Committee member) / Kosut, Oliver (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor)

Created2014-05

Audio Waveform Sample SVD Compression and Impact on Performance

Description

Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.

ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)

Created2021-05