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Noise Robust
Bird Song
Classification, Recognition, and Detection
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-- a summary of
Wei Chu's work on bird
song processing and analysis |
Bird songs are important in the communication between birds
of specific species. A bird can listen to other birds and
classify them as conspecific or heterospecific, neighbor or
stranger, mate or non-mate, kin or non-kin [1]. It can also
sing to other birds for mate attraction, danger alert, or
territory defense [2]. Behavioral and ecological studies
could benefit from automatically detecting and identifying
species from acoustic recordings.
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RMBL-Robin database
A 78 minutes Robin song database collected by using a
close-field song meter (www.wildlifeacoustics.com) at the
Rocky Mountain Biological Laboratory near Crested Butte,
Colorado in the summer of 2009 [3]. The recorded Robin songs
are naturally corrupted by different kinds of background
noises, such as wind, water and other vocal bird species.
Non-target songs may overlap with target songs. Each song
usually consists of 2-10 syllables. The timing boundaries
and noise conditions of the syllables and songs, and human
inferred syllable patterns are annotated.
The database is used for bird song detection. It can be downloaded from
here. To reference the RMBL-Robin
database, please use the following:
W. Chu, D.T. Blumstein, “Noise
robust bird song detection using
syllable pattern-based hidden Markov
models,” ICASSP 2011, pp. 345-348.
Please reference RMBL-Robin database when using it.
Antbird database
Researchers from the Ecology and Evolutionary Biology
department at UCLA recorded 127 minutes long 3366 calls from
5 species of Antbirds (Barred Antshrike, Dusky Antbird,
Great Antshrike, Mexican Antthrush, Dot-winged Antwren) in a
Mexican rainforest [4]. Different kinds of background noises
are observed in the recordings, such as other bird chirps
and insect sounds. The calls are 0.5 - 5.0 seconds long.
The database is used for bird species classfication. If interested in
using it, please contact Prof.
Charles Taylor.
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• W. Chu and A. Alwan, “fbEM:
a filter bank EM algorithm for
the joint optimization of
features and acoustic model
parameters in bird call
classification,” Interspeech 2012,
pp. 1993-1996. [poster]
• W. Chu, D.T. Blumstein, “Noise
robust bird song detection using
syllable pattern-based hidden Markov
models,” ICASSP 2011, pp. 345-348. [poster]
[database]
• W. Chu, A. Alwan, “A
correlation-maximization denoising filter used as an
enhancement frontend for noise robust bird call
classification,” InterSpeech 2009, pp. 2831-2834. [slides] |
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Supported in part by NSF.
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[1] P. Marler, “A comparative approach to
vocal learning: song development in white-crowned sparrows,”
J Comp Physiol Psychol, vol. 71, pp. 1–25, 1970.
[2] C. K. Catchpole and P. J. B. Slater, Bird Song:
Biological Themes and Variations, Cambridge University
Press, New York, 1995.
[3] W. Chu and D.T. Blumstein, “Noise
robust bird song detection using
syllable pattern-based hidden Markov
models,” ICASSP 2011, pp. 345-348. [poster]
[4] V. Trifa, A. Kirschel, and C. E. Taylor, “Automated
species recognition of antbirds in aMexican rainforest using
hidden Markov models,” The Journal of the Acoustical Society
of America, vol. 123, no. 4, pp. 2424–2431, 2008.
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last
updated: May 26, 2011. |
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