Noise Robust Bird Song
Classification, Recognition, and Detection
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.
This work is supported in part
by NSF.
Bird song, classification,
recognition, detection
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.
Kantapon Kaewtip, Lee Ngee Tan, Abeer Alwan, Charles E.Taylor, "
A
robust automatic bird phrase classifier using dynamic time-warping with
prominent region identification",
ICASSP 2013, pp. 768-772.
L. N. Tan, G. Kossan, M. L. Cody, C. E. Taylor, A. Alwan, "
A
Sparse Representation-based Classifier for In-set Bird Phrase
Verification and Classification with Limited Training Data,"
ICASSP
2013, pp. 763-767.
L. N. Tan, K. Kaewtip, M. L. Cody, C. E. Taylor, and A. Alwan, "
Evaluation
of a Sparse Representation-Based Classifier For Bird Phrase
Classification Under Limited Data Conditions",
Interspeech 2012,
pp. 2522-2525.
Wei Chu and Abeer Alwan, "
FBEM:
A Filter Bank EM Algorithm for the Joint Optimization Of Features and
Acoustic Model Parameters In Bird Call Classification",
ICASSP
2012, pp. 1993-1996.
W. Chu, D.T. Blumstein, “
Noise
robust bird song detection using syllable pattern-based hidden Markov
models,” ICASSP 2011,
pp. 345-348.
W. Chu and A. Alwan, "
A
Correlation-Maximization Denoising Filter Used as an Enhancement
Frontend for Noise Robust Bird Call Classification,"
InterSpeech
2009, pp. 2831-2834. [
slides]
[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.
Useful information::
Web links to software for bioacoustical
data processing
Examples of large scale automated
acoustic classification
Reference:
BIOACOUSTICAL
MONITORING IN TERRESTRIAL ENVIRONMENTS, Kurt M. Fristrup and Dan
Mennitt, Acoustics Today, July 2012.
Back to SPAPL Home Page.
Abeer Alwan
(alwan@ee.ucla.edu)
