Advances in computer technology have enabled the collection, digitization and
automated processing of huge archives of bioacoustic sound. Many of the tools previ-
ously used in bioacoustics work well with small to medium-sized audio collections, but
are challenged when processing large collections of tens of terabytes to petabyte size.
In this thesis, a system is presented that assists researchers to listen to, view, anno-
tate and run advanced audio feature extraction and machine learning algorithms on
these audio recordings. This system is designed to scale to petabyte size. In addition,
this system allows citizen scientists to participate in the process of annotating these
large archives using a casual game metaphor. In this thesis, the use of this system
to annotate a large audio archive called the Orchive will be evaluated. The Orchive
contains over 20,000 hours of orca vocalizations collected over the course of 30 years,
and represents one of the largest continuous collections of bioacoustic recordings in
the world. The effectiveness of our semi-automatic approach for deriving knowledge
from these recordings will be evaluated and results showing the utility of this system
will be shown. / Graduate / 0984 / sness@sness.net
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/5109 |
| Date | 23 December 2013 |
| Creators | Ness, Steven |
| Contributors | Tzanetakis, George |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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