| Issue |
MATEC Web Conf.
Volume 154, 2018
The 2nd International Conference on Engineering and Technology for Sustainable Development (ICET4SD 2017)
|
|
|---|---|---|
| Article Number | 03015 | |
| Number of page(s) | 5 | |
| Section | Computer Sciences | |
| DOI | https://doi.org/10.1051/matecconf/201815403015 | |
| Published online | 28 February 2018 | |
Syllables sound signal classification using multi-layer perceptron in varying number of hidden-layer and hidden-neuron
1
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: domykristomo@gmail.com
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/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.