KLASIFIKASI AUDIO MENGGUNAKAN WAVELET TRANSFORM DAN NEURAL NETWORK

Yulianto Mustaqim, Ema Utami, Suwanto Raharjo

Sari


Biodiversity that exists in nature shows the overall variation between living things both from the smallest levels, namely genes, species and eskosistem. One animal with a fairly high level of variation, namely birds chirping. Chirping has an identifier for each type both of the color of the feather, body shape, shape of the beak, food, how to find food and the most obvious is the difference in the chirping of birds. The problem faced is the number of species of birds chirping that are almost similar to each other so the introduction of birds with sound becomes quite difficult. This makes the introduction of birds with sound requires a special technique. The techniques used are transform wavelets and neural networks. At the end of the study, obtained Wavelet Package Decomposition extraction with training data used as many as 500 data. There are two preprocessing methods that are done by cutting and resampling (downsampling). The most optimal number of neurons to be used in hidden layers is 256 neurons with 500 epochs. The highest accuracy is 88.6% with momentum 0.2, learning rate 0.2 and wavelet daubechies2 while the lowest accuracy is 74.2% with momentum 0.8, learning rate 0.8 and wavelets haar.

 

Keywords: Classification, Neural Network, Wavelet Transform, Haar, Daubechies2


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Referensi


Sound Research Laboratories. (1976). Practical Building Acoustics. New York: Halstead Press.

Egan, M. D. (1972). Architectural Acoustics. New York: McGraw-Hill.

Al-Irhaim, Y. F., & Saeed, E. G. (2010). Arabic Word Recognition Using Wavelet Neural Network. Scientific Conference on Information Technology , 29-30.

Waluyanti, S. (2018, November 2). Teknik Audio Video. Retrieved from Teknik Audio Video: http://staffnew.uny.ac.id/upload/131635621/pendidikan/BAB+I+DASAR+SINYAL+AUDIO-EDIT.pdf

Defiyanti, S., & Jajuli, M. (2015). Integrasi Metode Klasifikasi Dan Clustering dalam Data Mining. Konferensi Nasional Informatika (KNIF) .

Zahid, S., Hussain, F., Rashid, M., Yousaf, M. H., & Habib, H. A. (2015, April 16). Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods. Hindawi Publishing Corporation Mathematical Problems in Engineering .

Lerch, A. (2012). An Introduciton to Audio Content Analysis (Applications in Signal Processing and Music Informatics). . New Jersey: John Wiley & Sons, inc.

Suyanto. (2017). Data Mining untuk Klasifikasi dan Klasterisasi Data. Bandung, Indonesia: Penerbit INFORMATIKA.

Haykin, S. (1999). Neural Networks - A Comprehensive Foundation (2nd Edition ed.). Delhi, India: Pearson Education (Singapore) Pte. Ltd., Indian Branch.

Burrus, C. S., Gopinath, R. A., & Guo, H. (1998). Introduction to Wavelets and Wavelet Transforms. Upper Saddle River, New Jersey, United States of America: Prentice Hall.

Fugal, D. L. (2009). Conceptual Wavelets in Digital Signal Processing. San Diego, Calofornia, Untied States of America: Space and Signals Technical Publishing.

Polikar, R. (2018, Oktober 2018). The Wavelet Tutorial. Retrieved from Fundamental Concepts and an Overview of The Wavelet Theory: http://web.iitd.ac.in/~sumeet/WaveletTutorial.pdf

El-Zaghmouri, B. M. (2015, Mei 23). Speech Recognition Using Neural Networks. . International Conference on Computing, Communication and Control Engineering (IC4E) .

Siva, Sundar, H., Siddharth, Nithin, & Rajesh. (2018). Classification of Arrhythmia using Wavelet Transform and Neural Network Model. Journal of Bioengineering and Biomedical Science , Vol 8.

Mansouri, B. Z., Mirvaziri, H., & Sadeghi, F. (2016). Designing and Implementing of Intelligent Emotional Speech Recognition with Wavelet and Neural Network. International Journal of Advance Computer Science and Applications (IJACSA) , Vol 7.

Shi, Y., Wang, G., Niu, J., Zhang, Q., Cai, M., Sun, B., et al. (2018). Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform. International Journal of Biological Sciences , Vol 14, 938-945.

Gogus, F. Z., & Karlik, B. (2015, Desember). Classification of Asthmatic Breath Sounds by Using Wavelet Transforms and Neural Networks. Engineering and Technology Publishing , 106-111.


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