Open Access
| Issue |
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 7 | |
| Section | Session 2: Bioinformatics | |
| DOI | https://doi.org/10.1051/itmconf/20171202002 | |
| Published online | 05 September 2017 | |
- http://web.stanford.edu/group/stanfordbirds/SUFRAME.html, accessed on April 25, 2017. [Google Scholar]
- M. Lopes, L. Gioppo, T. Higushi, C. Kaestner, C. Silla, A. Koerich, “Automatic bird species identification for large number of species,” 2011 IEEE International Symposium on Multimedia (ISM), pp.117–122, 5-7 Dec. 2011. [Google Scholar]
- M. Lopes, A. Lameiras Koerich, C. Nascimento Silla, C. Alves Kaestner, “Feature set comparison for automatic bird species identification,” in 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 965–970, 9-12 Oct. 2011 [Google Scholar]
- L. Deng and D. O’Shaughnessy. Speech processing: a dynamic and optimization-oriented approach. Marcel Dekker. pp. 41–48. ISBN 0-8247-4040-8, 2003. [Google Scholar]
- Speech Technology: A Practical Introduction. Topic: Spectrogram, Cepstrum and Mel-Frequency Analysis, http://www.speech.cs.cmu.edu/15-492/slides/03_mfcc.pdf [Google Scholar]
- C. Chou and H. Ko, “Automatic Birdsong Recognition with MFCC Based Syllable Feature Extraction”. In: CH. Hsu, L.T. Yang, J. Ma, C. Zhu (eds) Ubiquitous Intelligence and Computing. UIC 2011. Lecture Notes in Computer Science, vol 6905. Springer, Berlin, Heidelberg. [Google Scholar]
- Practical Introduction to Frequency-Domain Analysis. MathWorks Online Documentation of Signal Processing Toolbox. Online Web Resources. [Google Scholar]
- C. Lee, Y. Lee, and R. Huang. “Automatic recognition of bird songs using cepstral coefficients.” Journal of Information Technology and Applications 1, 1, 17–23, 2006. [Google Scholar]
- S. E. Anderson, A. S. Dave, and D. Margoliash. Template-based automatic recognition of birdsong syllables from continuous recordings. J. Acoust. Soc. Am., 100(2):1209–1219, 1996. ISSN 0001–4966. [CrossRef] [Google Scholar]
- P. Somervuo, A. Harma, S. Fagerlund. “Parametric representations of bird sounds for automatic species recognition“, IEEE Transactions on Audio, Speech, and Language Processing 14, 6: 2252–2263, 2006. [CrossRef] [Google Scholar]
- C. Lee, S. Hsu, J. Shih and C. Chou, “Continuous birdsong recognition using Gaussian mixture modeling of image shape features.” IEEE Transactions on Multimedia, 15, 2: 454–464, 2013. [CrossRef] [Google Scholar]
- C-H. Chou and P-H. Liu, “Bird Species Recognition by Wavelet Transformation of a Section of Birdsong“, Symp. and Workshop Ubiq., Auton. Trusted Comput., Brisbane, Australia, pp.189–193, July 2009. [Google Scholar]
- L. Neal, F. Briggs, R. Raich and X. Fern. “Time-frequency segmentation of bird song in noisy acoustic environments.” 2011 IEEE International Conference on. Acoustics, Speech and Signal Processing (ICASSP), 2011. DOI: 10.1109/ICASSP.2011.5946906 [Google Scholar]
- J. Springer, Z. Duan, and B. Pardo. “Approaches to multiple concurrent species bird song recognition.” The 2nd International Workshop on Machine Listening in Multisource Environments, 2013. http://www.ece.rochester.edu/~zduan/resource/SpringerEtal_BirdSongRecognition_ChiME13.pdf [Google Scholar]
- H. Zhao, and H. Malik. “Audio recording location identification using acoustic environment signature.” IEEE Transactions on Information Forensics and Security 8, 11 (2013): 1746–1759. [CrossRef] [Google Scholar]
- H. Goeau, H. Glotin, W. Vellinga, R. Planque, A. Rauber and A. Joly. LifeCLEF Bird Identification Task 2014. https://hal.inria.fr/hal-01088829/file/CLEF2014wn-Life-GoeauEt2014a.pdf [Google Scholar]
- O. Dufour, T. Artieres, H. Glotin and P. Giraudet (2014). “Clusterized mel filter cepstral coefficients and support vector machines for bird song identification“, In. Proc. 1st International Workshop on Machine Learning for Bioacoustics, joint to The 30th International Conference on Machine Learning (ICML 2013) Atlanta, USA, June, 2013, pages 89–93. [Google Scholar]
- D. P. W. Ellis. PLP and RASTA (and MFCC, and inversion) in Matlab, 2005. Online web resource. [Google Scholar]
- R. Polikar, “Ensemble learning.” Ensemble machine learning. Springer US, 2012. 1–34. [Google Scholar]
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