journal article Jul 17, 2014

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

PeerJ Vol. 2 pp. e488 · PeerJ
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References
43
[1]
Acevedo "Automated classification of bird and amphibian calls using machine learning: a comparison of methods" Ecological Informatics (2009) 10.1016/j.ecoinf.2009.06.005
[2]
$rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

M. Aharon, M. Elad, A. Bruckstein

IEEE Transactions on Signal Processing 2006 10.1109/tsp.2006.881199
[3]
Aide "Real-time bioacoustics monitoring and automated species identification" PeerJ (2013) 10.7717/peerj.103
[4]
Anderson "Template-based automatic recognition of birdsong syllables from continuous recordings" Journal of the Acoustical Society of America (1996) 10.1121/1.415968
[5]
Ballmer (2013)
[6]
Bates lme4: linear mixed-effects models using Eigen and S4 (2014)
[7]
Representation Learning: A Review and New Perspectives

Y. Bengio, A. Courville, P. Vincent

IEEE Transactions on Pattern Analysis and Machine... 2013 10.1109/tpami.2013.50
[8]
Random Forests

Leo Breiman

Machine Learning 2001 10.1023/a:1010933404324
[9]
Briggs "Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach" Journal of the Acoustical Society of America (2012) 10.1121/1.4707424
[10]
Briggs "Audio classification of bird species: a statistical manifold approach" (2009)
[11]
An empirical comparison of supervised learning algorithms

Rich Caruana, Alexandru Niculescu-Mizil

Proceedings of the 23rd international conference o... 2006 10.1145/1143844.1143865
[12]
Coates "Learning feature representations with k-means" (2012) 10.1007/978-3-642-35289-8_30
[13]
Damoulas "Bayesian classification of flight calls with a novel dynamic time warping kernel" (2010) 10.1109/icmla.2010.69
[14]
Davis "Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences" IEEE Transactions on Acoustics, Speech and Signal Processing (1980) 10.1109/tassp.1980.1163420
[15]
Dieleman "Multiscale approaches to music audio feature learning" (2013)
[16]
Digby "A practical comparison of manual and autonomous methods for acoustic monitoring" Methods in Ecology and Evolution (2013) 10.1111/2041-210x.12060
[17]
Erhan "Why does unsupervised pre-training help deep learning?" Journal of Machine Learning Research (2010)
[18]
An introduction to ROC analysis

Tom Fawcett

Pattern Recognition Letters 2006 10.1016/j.patrec.2005.10.010
[19]
Fodor "The ninth annual MLSP competition: first place" (2013) 10.1109/mlsp.2013.6661932
[20]
Fox "Call-independent identification in birds" PhD thesis (2008)
[21]
Glotin (2013)
[22]
Goëau "LifeCLEF bird identification task 2014" (2014)
[23]
Hausberger "Neuronal bases of categorization in starling song" Behavioural Brain Research (2000) 10.1016/s0166-4328(00)00191-1
[24]
Ito "Dynamic programming matching as a simulation of budgerigar contact-call discrimination" Journal of the Acoustical Society of America (1999) 10.1121/1.424591
[25]
Jafari "Fast dictionary learning for sparse representations of speech signals" IEEE Journal of Selected Topics in Signal Processing (2011) 10.1109/jstsp.2011.2157892
[26]
Laiolo "The emerging significance of bioacoustics in animal species conservation" Biological Conservation (2010) 10.1016/j.biocon.2010.03.025
[27]
Lakshminarayanan "A syllable-level probabilistic framework for bird species identification" (2009)
[28]
Lee "Automatic classification of bird species from their sounds using two-dimensional cepstral coefficients" IEEE Transactions on Audio and Speech and Language Processing (2008) 10.1109/tasl.2008.2005345
[29]
Least squares quantization in PCM

S. Lloyd

IEEE Transactions on Information Theory 1982 10.1109/tit.1982.1056489
[30]
McFee "More like this: machine learning approaches to music similarity" PhD thesis (2012)
[31]
McIlraith "Birdsong recognition using backpropagation and multivariate statistics" IEEE Transactions on Signal Processing (1997) 10.1109/78.650100
[32]
Olshausen "Sparse coding of sensory inputs" Current Opinion in Neurobiology (2004) 10.1016/j.conb.2004.07.007
[33]
Pedregosa "Scikit-learn: machine learning in Python" Journal of Machine Learning Research (2011)
[34]
Potamitis "Automatic classification of a taxon-rich community recorded in the wild" PLoS ONE (2014) 10.1371/journal.pone.0096936
[35]
R Core Team (2012)
[36]
Ranft "Natural sound archives: past, present and future" Anais da Academia Brasileira de Ciências (2004) 10.1590/s0001-37652004000200041
[37]
Selin "Wavelets in recognition of bird sounds" EURASIP Journal on Applied Signal Processing (2007) 10.1155/2007/51806
[38]
Stowell "Birdsong and C4DM: a survey of UK birdsong and machine recognition for music researchers" Technical report C4DM-TR-09-12 (2010)
[39]
Stowell "Feature design for multilabel bird song classification in noise (nips4b challenge)" (2013)
[40]
Stowell "Large-scale analysis of frequency modulation in birdsong databases" Methods in Ecology and Evolution (2014) 10.1111/2041-210x.12223
[41]
Theunissen "Auditory processing of vocal sounds in birds" Current Opinion in Neurobiology (2006) 10.1016/j.conb.2006.07.003
[42]
Tsoumakas "Mining multi-label data" (2010)
[43]
Yue "A support vector method for optimizing average precision" (2007)
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Published
Jul 17, 2014
Vol/Issue
2
Pages
e488
Cite This Article
Dan Stowell, Mark D. Plumbley (2014). Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ, 2, e488. https://doi.org/10.7717/peerj.488
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