PERLUASAN METODE MFCC 1D KE 2D SEBAGAI ESKTRAKSI CIRI PADA SISTEM IDENTIFIKASI PEMBICARA MENGGUNAKAN HIDDEN MARKOV MODEL (HMM)
| Main Authors: | Agus Buono; Departemen Ilmu Komputer, FMIPA, IPB, Kampus IPB Darmaga, Bogor 16680, Wisnu Jatmiko; Laboratorium Kecerdasan Komputasional, Fakultas Ilmu Komputer, Universitas Indonesia, Depok 16424, Benyamin Kusumoputro; Laboratorium Kecerdasan Komputasional, Fakultas Ilmu Komputer, Universitas Indonesia, Depok 16424 |
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| Format: | Article application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Directorate of Research and Community Engagement, Universitas Indonesia
, 2010
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| Subjects: | |
| Online Access: |
http://journal.ui.ac.id/index.php/science/article/view/12265 |
Daftar Isi:
- In this paper, we introduce an extension of Mel-Frequency Cepstrum Coefficients (1D-MFCC) methodology to bispectrum data, referred to as 2D-MFCC, for feature extraction. 2D-MFCC is based on 2D bispectrum data rather than 1D spectrum vector yielded by Fourier transform, so the filter in 1D-MFCC must be extend to 2D filter and using 2D cosine transform to get the mel-cepstrum coefficients from the filtered bispectrum values. Based on 2D-MFCC, we develop a speaker recognition system with Hidden Markov Model (HMM) as classifier. The experimental results show that the recognition rate is around 88%, 92% and 99% for 20, 40 and 60 data training, respectively.