Simulasi Ekstraksi Fitur Suara menggunakan Mel-Frequency Cepstrum Coefficient

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Published: Jul 6, 2022
DOI: https://doi.org/10.34128/jsi.v8i1.357
Keywords:
Simulasi, Windowing,Fourier, Ektraksi

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Santoso Santoso
Universitas 17 Agustus 1945 Surabaya
Ratna Hartayu
Choirul Anam
Dimas Abdul Aziz

Abstract

Berbicara adalah cara komunikasi yang paling mudah dan banyak digunakan antara manusia. Pengembangan antarmuka komputer manusia untuk membangun dialog serupa antara mesin dan manusia adalah inspirasi di balik sistem pengenalan suara. Salah satu algoritma tersebut adalah koefisien Cepstral frekuensi Mel. Makalah ini menjelaskan semua tahapan teknik MFCC bersama dengan deskripsi singkat dari setiap proses. Dalam penelitian ini dijelaskan salah satu metode alternatif analisa pengenalan suara. Program simulasi menggunakan python. Penelitian ini menghasilkan analisa simulasi perubahan data sinyal suara, menggambarkan implementasi pengenalan pola suara. Dalam penelitian ini dikembangkan metode filter hamming window, fast fourier transform(FFT) dan MFCC

Article Details

Issue
Vol. 8 No. 1 (2022): Jurnal Sains dan Informatika
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Articles
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

References

[1] S. Gupta, J. Jaafar, W. F. wan Ahmad, and A. Bansal, ‘Feature Extraction Using Mfcc’, Signal Image Process. Int. J., vol. 4, no. 4, pp. 101–108, Aug. 2013, doi: 10.5121/sipij.2013.4408.
[2] D. Cao, X. Gao, and L. Gao, ‘An Improved Endpoint Detection Algorithm Based on MFCC Cosine Value’, Wirel. Pers. Commun., vol. 95, no. 3, pp. 2073–2090, Aug. 2017, doi: 10.1007/s11277-017-3958-0.
[3] A. Setiawan, A. Hidayatno, and R. R. Isnanto, ‘Aplikasi Pengenalan Ucapan dengan Ekstraksi Mel-Frequency Cepstrum Coefficients (MFCC) Melalui Jaringan Syaraf Tiruan (JST) Learning Vector Quantization (LVQ) untuk Mengoperasikan Kursor Komputer’, Transm. J. Ilm. Tek. Elektro, vol. 13, no. 3, Art. no. 3, 2011, doi: 10.12777/transmisi.13.3.82-86.
[4] S. A. Alim and N. K. A. Rashid, Some Commonly Used Speech Feature Extraction Algorithms. IntechOpen, 2018. doi: 10.5772/intechopen.80419.
[5] J. Vanus et al., ‘Assessment of the Quality of Speech Signal Processing Within Voice Control of Operational-Technical Functions in the Smart Home by Means of the PESQ Algorithm’, IFAC-Pap., vol. 51, no. 6, pp. 202–207, Jan. 2018, doi: 10.1016/j.ifacol.2018.07.154.
[6] L.-M. Lee and F.-R. Jean, ‘Adaptation of Hidden Markov Models for Recognizing Speech of Reduced Frame Rate’, IEEE Trans. Cybern., vol. 43, no. 6, pp. 2114–2121, Dec. 2013, doi: 10.1109/TCYB.2013.2240450.
[7] D. Deshwal, P. Sangwan, and D. Kumar, ‘Feature Extraction Methods in Language Identification: A Survey’, Wirel. Pers. Commun., vol. 107, no. 4, pp. 2071–2103, Aug. 2019, doi: 10.1007/s11277-019-06373-3.
[8] ‘Handbook of digital signal processing: engineering applications’, Choice Rev. Online, vol. 26, no. 02, pp. 26-0940-26–0940, Oct. 1988, doi: 10.5860/CHOICE.26-0940.
[9] T. Mustofa, ‘Implementation Speech Recognition for Robot Control Using MFCC and ANFIS’, J. Telemat. Inform., vol. 5, no. 2, Art. no. 2, Sep. 2017, doi: 10.12928/jti.v5i2.
[10] H. Heriyanto, S. Hartati, and A. E. Putra, ‘EKSTRAKSI CIRI MEL FREQUENCY CEPSTRAL COEFFICIENT (MFCC) DAN RERATA COEFFICIENT UNTUK PENGECEKAN BACAAN AL-QUR’AN’, Telematika, vol. 15, no. 2, p. 99, Oct. 2018, doi: 10.31315/telematika.v15i2.3123.
[11] R. Hidayat, A. Bejo, and I. Muchlizar, ‘SISTEM PENGENALAN SUARA SECARA REAL-TIME MENGGUNAKAN PEMROGRAMAN C’, p. 7, 2019.
[12] M. Akhtar, ‘Text Independent Biometric Authentication System Based On Voice Recognition’, Biom. Authentication, p. 60, 2017.