[1]
J. Bishop, H. McKay, D. Parrot, and J. Allan, Review of international research literature regarding the effectiveness of auditory bird scaring techniques and potential alternatives. Central Science Laboratories, (2003).
Google Scholar
[2]
I. Potamitis, S. Ntalampiras, O. Jahn, and K. Riede, Automatic bird sound detection in long realfield recordings: Applications and tools, Applied Acoustics, vol. 80, pp.1-9, 2014. [Online].
DOI: 10.1016/j.apacoust.2014.01.001
Google Scholar
[3]
L. Klein, R. Mino, M. Hovan, P. Antonik, and G. Genello, MMW radar for dedicated bird detection at airports and airfields, in Radar Conference, 2004. EURAD. First European, Oct 2004, pp.157-160.
Google Scholar
[4]
C. Pornpanomchai, M. Homnan, N. Pramuksan, and W. Rakyindee, Smart scarecrow, in Measuring Technology and Mechatronics Automation (ICMTMA), 2011 Third International Conference on, vol. 3, Jan 2011, pp.294-297.
DOI: 10.1109/icmtma.2011.644
Google Scholar
[5]
V. Viswanathan, J. Makhoul, R. M. Schwartz, and A. Huggins, Variable frame rate transmission: A review of methodology and application to narrow-band LPC speech coding, Communications, IEEE Transactions on, vol. 30, no. 4, pp.674-686, Apr (1982).
DOI: 10.1109/tcom.1982.1095523
Google Scholar
[6]
R. Sun, Y. Marye, and H. -A. Zhao, FFT based automatic species identification improvement with 4-layer neural network, in Communications and Information Technologies (ISCIT), 2013 13th International Symposium on, Sept 2013, pp.513-516.
DOI: 10.1109/iscit.2013.6645912
Google Scholar
[7]
W. R. Gardner and B. Rao, Theoretical analysis of the high-rate vector quantization of LPC parameters, Speech and Audio Processing, IEEE Transactions on, vol. 3, no. 5, pp.367-381, Sep (1995).
DOI: 10.1109/89.466658
Google Scholar
[8]
C. Rader and N. Brenner, A new principle for fast Fourier transformation, Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 24, no. 3, pp.264-266, Jun (1976).
DOI: 10.1109/tassp.1976.1162805
Google Scholar
[9]
J. Markel and A. Gray, Linear Prediction of Speech. Springer Berlin Heidelberg, (1976).
Google Scholar
[10]
H. Hermansky, Perceptual linear predictive (PLP) analysis for speech recognition, Journal of the Acoustical Society of America, (1990).
DOI: 10.1121/1.399423
Google Scholar
[11]
S. Davis and P. Mermelstein, Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences, Acoustics, Speech and Signal Processing, IEEE Transactions on, vol. 28, no. 4, pp.357-366, Aug (1980).
DOI: 10.1109/tassp.1980.1163420
Google Scholar
[12]
A. McIlraith and H. Card, Birdsong recognition with DSP and neural networks, in WESCANEX 95. Communications, Power, and Computing. Conference Proceedings., IEEE, vol. 2, May 1995, pp.409-414 vol. 2.
DOI: 10.1109/wescan.1995.494065
Google Scholar
[13]
S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, (1999).
Google Scholar
[14]
H. Nguyen, N. Prasad, and C. Walker, A First Course in Fuzzy and Neural Control. Chapman and Hall/CRC, (2003).
Google Scholar
[15]
M. Hagan and M. Menhaj, Training feedforward networks with the Marquardt algorithm, Neural Networks, IEEE Transactions on, vol. 5, no. 6, pp.989-993, Nov (1994).
DOI: 10.1109/72.329697
Google Scholar
[16]
E. Kiktova, M. Lojka, M. Pleva, J. Juhar, and A. Cizmar, Comparison of different feature types for acoustic event detection system, in Multimedia Communications, Services and Security, ser. Communications in Computer and Information Science, A. Dziech and A. Czyzewski, Eds. Springer Berlin Heidelberg, 2013, vol. 368, pp.288-297.
DOI: 10.1007/978-3-642-38559-9_25
Google Scholar
[17]
E. Kiktova-Vozarikova, J. Juhar, and A. Cizmar, Feature selection for acoustic events detection, Multimedia Tools and Applications, pp.1-21, (2013).
DOI: 10.1007/s11042-013-1529-2
Google Scholar