Research Article
Voice Clustering Gender Using Fuzzy Possibilistic C-Means Standard
@INPROCEEDINGS{10.4108/eai.24-10-2018.2280623, author={Arif Setiawan and Pratomo Setiaji and Putri Kurnia Handayani and Noor Latifah}, title={Voice Clustering Gender Using Fuzzy Possibilistic C-Means Standard}, proceedings={The 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus}, publisher={EAI}, proceedings_a={ICCSET}, year={2018}, month={11}, keywords={voice clustering time domain fuzzy possibilistic c-means standard short time energy zero crossing rate}, doi={10.4108/eai.24-10-2018.2280623} }- Arif Setiawan
Pratomo Setiaji
Putri Kurnia Handayani
Noor Latifah
Year: 2018
Voice Clustering Gender Using Fuzzy Possibilistic C-Means Standard
ICCSET
EAI
DOI: 10.4108/eai.24-10-2018.2280623
Abstract
To recognize a voice pattern the computer requires a standard and logical mechanism. The main problem is how process acquisition data by generating a number of numerical data are representative and consistent.Voice recognition system use feature extraction techniques based on domain time with the two methods are short-time energy and zero crossing rate. Steps being taken is prepared ten audios, process feature extraction method based on domaintime, using a Short Time Energy, Zero Crossing Rate, and clustering using Fuzzy Possibilistic C-Means Standard. Step method voice recognition are input transducer for analyzing input of electronic signal, prepossessing to add signal condition including signal amplification, spectrum analysis and digital conversion, feature extraction to comparing template matching, response selector for selecting input pattern in software using the technique of searching, sorting, least squares analysis, and output system to show result application process.