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    Automatic recognition and immersive representation of environmental soundscapes

    Author
    Morgan, Mallory M.
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    179255_Morgan_rpi_0185N_11341.pdf (3.143Mb)
    179256_morganThesis2018.avi (7.021Mb)
    179257_morganThesis2018.mp4 (19.21Mb)
    Other Contributors
    Braasch, Jonas; Xiang, Ning; Lokko, Mae-Ling;
    Date Issued
    2018-08
    Subject
    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.;
    Metadata
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    URI
    https://hdl.handle.net/20.500.13015/2269
    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;
    Access
    Restricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.;
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