Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features
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Abstract
This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.
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[1]
H. Heriyanto, T. Wahyuningrum, and G. F. Fitriana, “Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features”, INFOTEL, vol. 13, no. 2, pp. 84-93, May 2021.
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Informatics
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[32] Heriyanto, “Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient ( MFCC ) Feature Extraction and Frame Feature Selection,” Telematika: Jurnal Informatika dan Teknologi Informasi,vol. 18, no. 1, pp. 88–105, 2021.
[2] F. Syafria, A. Buono, and B. P. Silalahi, “Pengenalan Suara Paru-Paru dengan MFCC sebagai Ekstraksi Ciri dan Backpropagation sebagai Classifier”, J. Ilmu Komput Agri-Inf, vol. 3, no. 1, pp. 27-36, Jan. 2017.
[3] H. S. Manunggal, “Perancangan dan Pembuatan Perangkat Lunak Pengenalan Suara Pembicara Dengan Menggunakan Analisa MFCC Feature Extraction.,” Tugas Akhir Sarj. pada Jur. Tek. Inform. Fak. Teknol. Ind. Univ. Kristen Petra Surabaya, 2005.
[4] T. Chamidy, “Metode Mel Frequency Cepstral Coeffisients (MFCC) Pada klasifikasi Hidden Markov Model (HMM) Untuk Kata Arabic pada Penutur Indonesia,” Matics, vol. 8, no. 1, pp. 36–39, 2016.
[5] Irmawan, H. Hikmarika, D. W. Sari, and M. C. Tammimi, “Pengenalan Kata dengan Metode Linear Predictive Coding dan Jaringan Syaraf Tiruan Pada Mobile Robot,” in CITEE 2014, Yogyakarta, 2014.
[6] Thiang, H. Saputra “Sistem Pengenalan Kata dengan Menggunakan Linear Predictive Coding dan Nearest Neighbor Classifier,” Jurnal Teknik Elektro, vol. 5, no.1, pp. 19–24, Maret, 2005.
[7] A. M. Aibinu, M. J. E. Salami, A. R. Najeeb, J. F. Azeez, and S. M. A. K. Rajin, “Evaluating the effect of voice activity detection in isolated Yoruba word recognition system,” in 2011 4th Int. Conf. Mechatronics Integr. Eng. Ind. Soc. Dev. ICOM’11 - Conf. Proc., no. May, pp. 17–19, 2011, doi: 10.1109/ICOM.2011.5937134.
[8] S. Hidayat, R. Hidayat, and T. B. Adji, “Sistem Pengenal Tutur Bahasa Indonesia Berbasis Suku Kata Menggunakan MFCC, Wavelet Dan HMM,” in Conf. Inf. Technol. Electr. Eng., no. September, pp. 246–251, 2015.
[9] S. M. Widodo, E. Siswanto, and O. Sudjana, “Penerapan Metode Mel Frequency Ceptral Coefficient dan Learning Vector Quantization untuk Text-Dependent Speaker Identification,” Jurnal Telematika, vol. 11, no. 1, pp. 15–20, 2016.
[10] Suyanto and S. Hartati, “Design of Indonesian LVCSR using Combined Phoneme The Approaches of LVCSR,” Icts, pp. 191–196, 2013.
[11] S. Suyanto and A. E. Putra, “Automatic Segmentation of Indonesian Speech into Syllables using Fuzzy Smoothed Energy Contour with Local Normalization, Splitting, and Assimilation,” J. ICT Res. Appl., vol. 8, no. 2, pp. 97–112, 2014.
[12] R. Cahyarini, U. L. Yuhana, and A. Munif, “Rancang Bangun Modul Pengenalan Suara Menggunakan Teknologi Kinect,” J. Tek. Pomits, vol. 2, no. 1, pp. 1–5, 2013.
[13] M. K. A. S. . dan S. Hertiana Bethaningtyas, “Pengenalan Huruf Hijayyah Berbasis Pengolahan Sinyal Suara dengan Metode MFCC,” Momentum, vol.13, no. 2, pp. 49–52, Oktober,2017.
[14] Heriyanto, “Analisa Deteksi Huruf Hijaiyah Melalui Voice Recognition Menggunakan Kombinasi Energy,” Telematika, vol. 12, no. 01, pp. 11–22, 2015.
[15] M. Subali, M. Andriansyah, and C. Sinambela, “Analisis Frekuensi Dasar dan Frekuensi Formant dari Fonem Huruh Hijaiyah Untuk Pengucapan Makhraj Dengan Metode DTW,” in Pros. PESAT (Psikologi, Ekon. Sastra, Arsit. &Teknik Sipil), Depok, 2015, pp. -60-72.
[16] C. Goh and K. Leon, "Robust Computer Voice Recognition Using Improved MFCC Algorithm," in 2009 International Conference on New Trends in Information and Service Science, 2009, pp. 835-840.
[17] L. Muda, M. Begam, and I. Elamvazuthi, “Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques,” J. Computing, vol. 2, no. 3, pp. 138–143, 2010, [Online]. Available: http://arxiv.org/abs/1003.4083.
[18] M. W. . Sanjaya and Z. Salleh, “Implementasi Pengenalan Pola Suara Menggunakan Mel-Frequency Cepstrum Coefficients (Mfcc) Dan Adaptive Neuro-Fuzzy Inferense System (Anfis) Sebagai Kontrol Lampu Otomatis,” Al-HAZEN J. Phys., vol. 1, no. 1, pp. 1–19, 2014.
[19] Y. Miftahuddin and M. R. Hakim, “Coefficient Dan Dynamic Time Warping Untuk Pengenalan Nada Pada Alat Musik Bellyra,” pp. 120–127, 2017.
[20] A. R. Darma Putra, “Verifikasi Biometrika Suara Menggunakan Metode MFCC dan DTW,” LONTAR Komput. Biometrika, vol. 2, no. 1, pp. 8–21, 2011.
[21] D. Novianto and R. V. Yuliantari, “Pengenalan Isyarat Tutur Vokal Bahasa Indonesia Menggunakan Metode Dynamic Time Wraping ( Dtw ) Berbasis Fungsi Jarak,” Journal of Electrical Engineering, Computer and Information Technology, vol.1, no. 1, pp. 53–57, 2017.
[22] S. Martyna and S. Sudaryanto, “Penerapan Metode Particle Swarm Optimization pada Artificial Neural Network Backpropagation untuk Peramalan Penjualan Furniture pada CV. Octo Agung,” Skripsi, Fakultas Ilmu Komputer, UDINUS, 2015.
[23] S. B. Davis and P. Mermelstein, “Comparison of Parametric Representations for Monosyllabic Word Recognition in Continuously Spoken Sentences,” IEEE Trans. Acoust., vol. 28, no. 4, pp. 357–366, 1980.
[24] R. T. M. (Sim) narasimha Tokunbo Ogunfunmi, Speech and Audio Processing and Recognition, no. part 1. springer, 2015.
[25] J. H. and W. Holmes, Speech Synthesis and Recognition, Second Edition. 2003.
[26] J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, algorithms, and applications. 1996.
[27] A. E. Putra, “Frekuensi Cuplik pada FFT,” Tan Li, Process. Digit. Signal, vol. 1, 2008.
[28] D. Laha, Handbook of Computational Intelligence in Manufacturing and Production Manajemen. 2007.
[29] S. W. Smith, Digital signal processing, vol. 17, no. 2. 2000.
[30] K. R. R. Vladimir Britanak, Patrick C.Yip, Discrete Cosine and Sine Transform. 2007.
[31] Heriyanto, S. Hartati, and A. E. Putra, “Evaluation of Suitability of Voice Reading of Al-Qur’an Verses Based on Tajwid Using Mel Frequency Cepstral Coefficients (MFCC) and Normalization of Dominant Weight (NDW),” Adv. Image Video Process., vol. 6, no. 2, pp. 16-35, 2018.
[32] Heriyanto, “Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient ( MFCC ) Feature Extraction and Frame Feature Selection,” Telematika: Jurnal Informatika dan Teknologi Informasi,vol. 18, no. 1, pp. 88–105, 2021.