Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction

Authors

  • Diah Kumalasari Politeknik Negeri Samarinda
  • Arief Bramanto Wicaksono Putra Politeknik Negeri Samarinda
  • Achmad Fanany Onnilita Gaffar Politeknik Negeri Samarinda

Keywords:

Classification, Feature Extraction, K-Mean, Virtual Center of Gravity

Abstract

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%.

Author Biographies

Diah Kumalasari, Politeknik Negeri Samarinda

Information Technology

Arief Bramanto Wicaksono Putra, Politeknik Negeri Samarinda

Information Technology Department

Achmad Fanany Onnilita Gaffar, Politeknik Negeri Samarinda

Information Technology

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Published

2020-05-29

Issue

Vol. 14 No. 2 (2020): May 2020

Section

Articles

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