IDENTIFIKASI HEWAN BERDASARKAN POLA AKUSTIK DENGAN PRINSIP EKSTRAKSI WAVELET DAN KLASIFIKASI MULTI-LABEL JARINGAN SYARAF TIRUAN

Defrianto Defrianto, Titrawani Titrawani, Lazuardi Umar, Vepy Asyana

Abstract


An acoustic identification and classification system of frogs has been designed based on the principle of wavelet extraction and label classification using an artificial neural network (ANN). This system consists of electronic detection for frog audio as well as an interface using the MATLAB 2018b software as an ANN provider device. As input for the neural network, 5 types of frogs were used, namely the rock frog (Limnonectes macrodon), the blentung frog (Kaloula baleata), the hip frog (Limnonectesblythii), the rice field frog (Fejervarya cancrivora), and the trench frog. frog. frog (Fejervarya limnocharis). ), each with 12 sound samples. Before being inserted into the neural network, 3 levels of sound samples were extracted and denoised using wavelet symlet 3. Furthermore, in the neural network training process, 3 validation samples and 3 test samples were used. After training, the artificial neural network was able to identify the type of frog being tested.

Keywords


Frog; Wavelet Extraction; Artificial Neural Network

References


1. Malcolm, J. R., Liu, C., Neilson, R. P., Hansen, L., & Hannah, L. E. E. (2006). Global warming and extinctions of endemic species from biodiversity hotspots. Conservation Biology, 20(2), 538–548.

2. Canavero, A., Arim, M., Naya, D. E., Camargo, A., Rosa, I. D., & Maneyro, R. (2008). Calling activity patterns in an anuran assemblage: The role of seasonal trends and weather determinants. North-Western Journal of Zoology, 4(1), 29–41.

3. Bedoya, C., Isaza, C., Daza, J. M., & Lopez, J. D. (2014). Automatic recognition of anuran species based on syllable identification. Ecological Informatics, 24, 200–209.

4. Akmentins, M. S., Pereyra, L. C., Sanabria, E. A., & Vaira, M. (2015). Patterns of daily and seasonal calling activity of a direct-developing frog of the subtropical Andean forests of Argentina. Bioacoustics, 24(2), 89–99.

5. Huang, C. J., Yang, Y. J., Yang, D. X., & Chen, Y. J. (2009). Frog classification using machine learning techniques. Expert Systems with Applications, 36(2), 3737–3743.

6. Kurniati, H. (2014). Keberadaan kodok Polypedates discantus di Sumatera. Warta Herpetofauna, 7(3), 6–7.

7. Gingras, B. & Fitch, W. T. (2013). A three-parameter model for classifying anurans into four genera based on advertisement calls. The Journal of the Acoustical Society of America, 133(1), 547–559.

8. Ospina, O. E., Villanueva-Rivera, L. J., Corrada-Bravo, C. J., & Aide, T. M. (2013). Variable response of anuran calling activity to daily precipitation and temperature: implications for climate change. Ecosphere, 4(4), 1–12.

9. Aide, T. M., Corrada-Bravo, C., Campos-Cerqueira, M., Milan, C., Vega, G., & Alvarez, R. (2013). Real-time bioacoustics monitoring and automated species identification. PeerJ, 1, e103.

10. Xie, J., Towsey, M., Truskinger, A., Eichinski, P., Zhang, J., & Roe, P. (2015). Acoustic classification of australian anurans using syllable features. 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), 1–6.

11. Xie, J., Towsey, M., Zhang, J., & Roe, P. (2016). Adaptive frequency scaled wavelet packet decomposition for frog call classification. Ecological Informatics, 32, 134–144.

12. Duellman, W. E., Schlager, N., & Trumpey, J. E. (2003). Grzimek's Animal Life Encyclopedia; Volume 6: Amphibians. Thomson-Gale.

13. Xie, J., Towsey, M., Yasumiba, K., Zhang, J., & Roe, P. (2015). Detection of anuran calling activity in long field recordings for bio-acoustic monitoring. 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 1–6.

14. Puspita, W., Defrianto, D., & Soerbakti, Y. (2021). Prediksi Kadar Particulate Matter (PM10) Menggunakan Jaringan Syaraf Tiruan di Kota Pekanbaru. Komunikasi Fisika Indonesia, 18(1), 1–4.

15. Tyagi, H., Hegde, R. M., Murthy, H. A., & Prabhakar, A. (2006). Automatic identification of bird calls using spectral ensemble average voice prints. 2006 14th European Signal Processing Conference, 1–5.

16. Zhang, M. L., & Zhou, Z. H. (2013). A review on multi-label learning algorithms. IEEE transactions on knowledge and data engineering, 26(8), 1819–1837.




DOI: http://dx.doi.org/10.31258/jkfi.19.1.51-56

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