MATEC Web Conf.
Volume 154, 2018The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|Number of page(s)||5|
|Published online||28 February 2018|
Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron
Department of Electrical Engineering and Information Technology, Universitas Gadjah Mada, Grafika Street No. 2, Yogyakarta, 55281 Indonesia
2 Study Program of Computer Engineering, STMIK Akakom Yogyakarta, Raya Janti Street 143 Karang Jambe Yogyakarta, 55198 Indonesia
* Corresponding author: firstname.lastname@example.org
The research on signal processing of syllables sound signal is still the challenging tasks, due to non-stationary, speaker-dependent, variable context, and dynamic nature factor of the signal. In the process of classification using multi-layer perceptron (MLP), the process of selecting a suitable parameter of hidden neuron and hidden layer is crucial for the optimal result of classification. This paper presents a speech signal classification method by using MLP with various numbers of hidden-layer and hidden-neuron for classifying the Indonesian Consonant-Vowel (CV) syllables signal. Five feature sets were generated by using Discrete Wavelet Transform (DWT), Renyi Entropy, Autoregressive Power Spectral Density (AR-PSD) and Statistical methods. Each syllable was segmented at a certain length to form a CV unit. The results show that the average recognition of WRPSDS with 1, 2, and 3 hidden layers were 74.17%, 69.17%, and 63.03%, respectively.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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