A Method of Insect Recognition Based on Spectrogram

Article Preview

Abstract:

A novel approach to insect recognition is presented in this paper. The difference between the proposed method with traditional methods is that it starts from the perspective of image and combines voice processing algorithms with image processing algorithms. The classification is done based on voice activity detection (VAD) and spectrogram. We show, by means of example that this approach can recognize different insects correctly. However, despite the potential of correct recognition, further justification of the reliability of the method need to be provided by a larger scale of experiments. Hence, some improvements will be proposed latterly.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

362-366

Citation:

Online since:

August 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Muda, Lindasalwa, Mumtaj Begam, and I. Elamvazuthi. Voice recognition algorithms using mel frequency cepstral coefficient (mfcc) and dynamic time warping (dtw) techniques., arXiv preprint arXiv: 1003. 4083 (2010).

Google Scholar

[2] Bala, Anjali, Abhijeet Kumar, and Nidhika Birla. Voice command recognition system based on MFCC and DTW., International Journal of Engineering Science and Technology 2. 12 (2010): 7335-7342.

Google Scholar

[3] Kevin M. Coggins and Jose Principe, Detection and Classification of Insect Sounds in a Grain Silo using a Neural Network.

Google Scholar

[4] Nellenbach C. Chesmore E.D., Acoustic methods for the automated detection and identification of insects,. Acta Horticulturae (562): pp.223-231, (2001).

DOI: 10.17660/actahortic.2001.562.26

Google Scholar

[5] Czarnecki, K. R. Z. Y. S. Z. T. O. F., and M. A. R. E. K. Moszyński. Using concentrated spectrogram for analysis of audio acoustic signals., Hydroacoustics 15 (2012): 27-32.

Google Scholar

[6] Lampert, Thomas A., and Simon EM O'Keefe. A survey of spectrogram t rack detection algorithms., Applied acoustics 71. 2 (2010): 87-100.

DOI: 10.1016/j.apacoust.2009.08.007

Google Scholar

[7] Tanyer, S. Gökhun, and Hamza Ozer. Voice activity detection in nonstationary noise., IEEE Transactions on Speech and Audio Processing 8. 4 (2000): 478-482.

DOI: 10.1109/89.848229

Google Scholar

[8] Information on http: /www. ars. usda. gov/pandp/docs. htm?docid=10919#anastrepha%20suspensa.

Google Scholar

[9] Elad, Michael, and Michal Aharon. Image denoising via sparse and redundant representations over learned dictionaries., Image Processing, IEEE Transactions on 15. 12 (2006): 3736-3745.

DOI: 10.1109/tip.2006.881969

Google Scholar

[10] Chang, S. Grace, Bin Yu, and Martin Vetterli. Adaptive wavelet thresholding for image denoising and compression., Image Processing, IEEE Transactions on 9. 9 (2000): 1532-1546.

DOI: 10.1109/83.862633

Google Scholar