Animal sound activity detection using multi-class support vector machines

2011 4th International Conference on Mechatronics: Integrated Engineering for Industrial and Societal Development, ICOM'11 - Conference Proceedings

Volumn , Issue , 2011, Pages

  Astuti, W ^a   Aibinu, A M ^a   Salami, M J E ^a   Akmelawati, R ^a   Muthalif, Asan G A ^a  

^a INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA   (Malaysia)

Author keywords

Animal Sounds Activity detector; Earthquake; Mel frequency cepstral coefficients (MFCC)Natural disaster; Support Vector Machine (SVM)

Indexed keywords

ACTIVITY DETECTION; ANIMAL BEHAVIOR; FEATURES EXTRACTION; HUMAN BEING; INTEGRAL PART; MEL-FREQUENCY CEPSTRAL COEFFICIENTS; MULTI-CLASS SUPPORT VECTOR MACHINES; MYANMARS; NATURAL DISASTERS; SUPPORT VECTOR;

CLASSIFICATION (OF INFORMATION); DETECTORS; DISASTERS; EARTHQUAKES; MECHATRONICS; SPEECH RECOGNITION; SUPPORT VECTOR MACHINES;

ANIMALS;

EID: 79961176845     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICOM.2011.5937122     Document Type: Conference Paper

References (25)

1. http://www.commondreams.org/headlines04/1229-07.htm, accessed online April 2010.
2. Bhargava, N., Katiyar, V., K., Sharma, M.,L., Pradhan, P., (2009). Earhquake Prediction through Animal Behavior: A Review. Indian Journal of Biomechanics: Special Issue.
3. Thanassoulas, C., Klentos, V., (2010). How"Short" a "Short-term prediction" can be? A review of the case of Skyros Island, Greece, EQ (26/7/2001, Ms=6.1). Geophysics.1002.2162.
4. Thanassoulas, C., Klentos, V., Vervenniotis, G. Zymaris, N. (2009). Seismic electric precursors observer prior to the 6.3R Eq of July 1st 2009, Greek and their use in short-term earthquake prediction. Geophysics.1003.2772.
5. Hayakawa, M., T. Itoh, K. Hattori, and K. Yumoto, (2000): ULF electromagnetic precursors for an earthquake at Biak, Indonesia on February 17, 1996. Geophys. Res. Lett., 27, 1531-1534.
6. Ikeya, M. 2004. Earthquake and Animal: from Folk Legend to Science. World Scientific.
7. Thanassoulas, C., and Tsatsaragos, J., 2000. The earthquakes of Izmit, Turkey (Ms=7.5R, 17/8/1999) and Athens, Greece (Ms=5.9R, 07/09/1999) as being detected by precursory electrical signals (T=24h period oscillation). Open File report E3906, Inst. Geol. Min. Expl. (IGME), Athens, Greece, pp. 1-18.
8. Thanassoulas, C., Klentos, V., Vervebiotis, G., Zymaris, N. 2009. Seismic electric precursor observed prior to the 6.3R EQ of July 1th 2009, Greece and their use in shortterm earthquake prediction.
9. Uyeda, S., Nagao, T., Orihara, Y., Yamaguchi, T., Takahashi, I. 2000. Geoelectric potential change: Possible precursor to earthquake in Japan.
10. Buskirk,E.,R, Frohlich, C., Latham,G.,V,1981, 'Unusual Animal Behavior Before Earthquakes: A Review of Possible Sensory Mechanisms, Review of Geophysics and Space Physics, Vol.19,No.2,Pages 247-270. (Pubitemid 12437039)
11. Harma,A,(2003). Automatic Recognition of bird species based on sinusoidal modeling of syllables. In: Proc. Of IEEE Internat. Conf. on Acoustic, Speech and Signal Processing. CICASSP 03. Vol. 5, pp. 545-548.
12. Chou, C,. and Liu, P,. (2009). Bird Species Recogniiton by Wafelet Transformation of a Section of Birdsong. In: Proc. Of symposia and workshop on ubiquitous, Autonomis and Trusted Computing. Pp 189-193.
13. Miguel A. A., Carlos J. B., Hector C., Luis J.V., and Aide. T.M. (2009). Automated Classification of Bird and Amphibian calls using Machine learning: A comparison of Methods. Elsevier J. Ecological Informatics. Volume 4, Issue 4, pp 206-224.
14. Huang, C., Yang, Y., Yang, D., Chen, Y. (2009). Frog Classification Using Machine learning techniques. Elsivier J. Expert System with Application. Volume 36, Issue 2 Part 2, pp 3737-3743.
15. Nikerson, C.M., Bloom Field, L.,L., Dawson, M.,R.,W., Sturdy, C.,B. (2006). Artificial Neural Network Discrimination of Black-Capped Chickadee (poecile atricapillus) call notes. Appl. Acoust. 67(11-12), 1111-1117. (Pubitemid 44213720)
16. Fagerlund, S. (2007). Bird Species Recognition using Support Vector Machines. Eurasip J. Appl. Signal Process. (EURASIP JASP).8p.
17. Bardeli, R., Walff, D., Kurth, F., Koch, M., Tauchert, K.,H., Frommolt, K.,H. (2009). Detecting bird sounds in a complex acoustic environment and application to biocastic monitor. Elsivier J. Pattern Recognition Letters.
18. Cristianini,N., Shawe,T.,J. (2000) An introduction to Support Vector Machine and other kernel-based learning methods. Cambridge University Press
19. L.R.Rabiner, R.W. Schafer, Digital processing of speech signals. Prentice Hall, 1978.
20. Bannet.P.Kristin. C.Campbell, Support Vector Machines: Hype or Hallelujah. SIGKDD exploration. Vol. 2, 2000.
21. Jose Boris Sanchez, Speaker Identification based on an Integrated System Combining Cepstral Feature Extraction and Vector Quantization, (Master Thesis), The Florida State University College of Engineering, United State of America, 2005.
22. Bi-Acoustic: http://www.avisoft.com/sounds.htm
23. http://www.bird.cornell.edu/
24. http://www.math.sunysb.edu/~tony/gulls.html
25. http://thryomanes.tripod.com/B