Detection and Segmentation of Bird Song in Noisy Environments

Honors College Thesis

Public Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/honors_college_theses/73666651n

Descriptions

Attribute Name	Values
Creator	Neal, Lawrence W.
Abstract	Recent work in machine learning concerns the detection and identification of bird species from audio recordings of their vocalizations. Such analysis can yield valuable ecological information concerning the activity and distribution of species in the wild. Current species-identification methods require individual syllables of bird audio as input, but field-collected audio contains noise and simultaneous vocalizations. This thesis presents two supervised learning methods for identifying and separating individual syllables of bird vocalizations from field-recorded audio. The segments output by this process can be input into species classification algorithms, to yield useful ecological data.
License	All rights reserved
Resource Type	Honors College Thesis
Date Available	2012-06-27T19:07:24+00:00
Date Issued	2012-05-30
Degree Level	Bachelor's
Degree Name	Honors Bachelor of Science (H.B.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2012
Advisor	Fern, Xiaoli Z.
Committee Member	Jones, Julia Raich, Raviv
Non-Academic Affiliation	Oregon State University. Honors College
Rights Statement	In Copyright
Publisher	Oregon State University
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/30249

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