Automatic recognition and immersive representation of environmental soundscapes

Authors
Morgan, Mallory M.
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Other Contributors
Braasch, Jonas
Xiang, Ning
Lokko, Mae-Ling
Issue Date
2018-08
Keywords
Architectural sciences
Degree
MS
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
The amount of audio data required for bioacoustics monitoring and other applications is often too large to be manually sorted and analyzed. Consequently, automatic sound recognition techniques have to be applied to automatically (1) identify and extract relevant acoustic stimuli and (2) classify these stimuli after a training period. Using audio data collected in the field over a period of several months in Troy, NY, the ability of three different automatic sound recognition techniques at classifying environmental sound stimuli is compared. These three algorithms include a hidden Markov model, a multilayer perceptron, and a convolutional neural network pretrained on Google's Inception-v3. The most accurate algorithm, the convolutional neural network, is then used to automatically classify an unvalidated dataset to allow seasonal soundscape changes to be observed. The analysis can then be used to create an automated non-linear time-lapse to summarize the events that occurred and meaningfully re-represent them in our immersive virtual environment, the CRAIVE-Lab -- together with collected visual material.
Description
August 2018
School of Architecture
Department
School of Architecture
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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