ANALISIS PENGENALAN NADA GITAR MENGGUNAKAN METODE KLASIFIKASI DYNAMIC TIME WARPING (DTW)

Catur Budi Waluyo, Rian Mulyadi, Yenni Astuti

Abstract


Abstrak

Perkembangan teknologi pemrosesan sinyal digital telah memberikan kontribusi yang signifikan terhadap pengenalan nada gitar dengan menggunakan algoritma Dynamic Time Warping (DTW). Penelitian dimulai dengan perekaman suara, preprocessing, dan ekstraksi ciri dari data referensi dan uji. Pada penelitian ini karakteristik suara diekstraksi menggunakan metode Fast Fourier Transform (FFT). Langkah selanjutnya adalah membandingkan hasil ekstraksi ciri FFT menggunakan algoritma DTW, dan diambil nilai terkecil sebagai keluarannya. Pengujian dilakukan sebanyak sepuluh kali untuk setiap nada gitar yang direkam, dan akurasi pengenalan nada gitar sebesar 82,5% untuk nada yang diuji yaitu A, B, C, dan D.
Kata kunci: Dynamic Time Warping, Gitar, Ekstraksi Ciri

Abstract

The development of digital signal processing technology has contributed significantly to the recognition of guitar tones using the Dynamic Time Warping (DTW) algorithm. The research started with the sound recording, preprocessing, and feature extraction from the reference and test data. In this research, the sound characteristics were extracted using the Fast Fourier Transform (FFT) method. The next step involved comparing the results of FFT feature extraction using the DTW algorithm, with the smallest value being taken as the output. The test was ten times for each recorded guitar tone, and the accuracy in recognizing guitar tones was 82.5% for the tested tones, namely A, B, C, and D.
Keywords: Dynamic Time Warping, guitar, feature extraction


Keywords


Dynamic Time Warping, guitar, feature extraction

Full Text:

PDF

References


Anggita, T., Khotimah, W. N., & Suciati, N. (2018). Pengenalan Bahasa Isyarat Indonesia dengan Metode Dynamic Time Warping (DTW) menggunakan Kinect 2.0. Jurnal Teknik ITS, 7(1), A199-A202.

Firmansyah, R., Djamal, E. C., & Yuniarti, R. (2018, August). Identifikasi Nada Dari Sinyal Suara Alat Musik Instrumen Menggunakan Metode Mel Frequency Cepstrum Coefficients dan Hidden Markov Model. In Seminar Nasional Aplikasi Teknologi Informasi (SNATI).

Firmansyah, R., Djamal, E. C., & Yuniarti, R. (2018, August). Identifikasi Nada Dari Sinyal Suara Alat Musik Instrumen Menggunakan Metode Mel Frequency Cepstrum Coefficients dan Hidden Markov Model. In Seminar Nasional Aplikasi Teknologi Informasi (SNATI).

Izakian, H., Pedrycz, W., & Jamal, I. (2015). Fuzzy clustering of time series data using dynamic time warping distance. Engineering Applications of Artificial Intelligence, 39, 235-244.

Jiang, S., & Chen, Z. (2023). Application of dynamic time warping optimization algorithm in speech recognition of machine translation. Heliyon, 9(11).

Klasik, G., & Del Sal, A. TEKNIK PERMAINAN GITAR “LOVE THEME FROM CINEMA PARADISO” KARYA ENNIO MORRICONE ARANSEMEN ADRIANO DEL SAL.

Nurarinda, T. A. P., Sahertian, J., & Mahdiyah, U. (2020, December). Rancangan Sistem Identifikasi Jenis Burung Kicau Berdasarkan Suara Burung dengan Mel Frequency Cepstrum Coefficiens (MFCC). In Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) (Vol. 4, No. 1, pp. 237-241).

Nurhasanah, Y. I., Korio Utoro, R., & Nugraha, R. D. (2018). Pengenalan Pola Ucapan Kata Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Multimedia Interaktif.

P. V. Lopes Pires, F. Carneiro Travassos, E. B. Kapisch, L. Rodrigues Manso Silva, C. A. Duque and P. Fernando Ribeiro, "Novelty Detection Based on Dynamic Time Warping Similarity Metric Applied to Power Quality Signals," 2022 20th International Conference on Harmonics & Quality of Power (ICHQP), Naples, Italy, 2022, pp. 1-6, doi: 10.1109/ICHQP53011.2022.9808685

Prayoga, N. F. I., Astuti, Y., & Waluyo, C. B. (2019). Analisis Speaker Recognition Menggunakan Metode Dynamic Time Warping (DTW) Berbasis Matlab. Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls, 1(1), 77-85.

S. Datta, C. K. Karmakar and M. Palaniswami, "Averaging Methods using Dynamic Time Warping for Time Series Classification," 2020 IEEE Symposium Series on Computational Intelligence (SSCI), Canberra, ACT, Australia, 2020, pp. 2794-2798, doi: 10.1109/SSCI47803.2020.9308409.

Suryadikarsa, F. M., Nurhasanah, Y. I., & Dewi, I. A. (2020). Identifikasi Nada antara Suling Sunda dan Suling Rekorder dengan Menggunakan Metode Frequency Cpstral Coefficients (MFCC) dan Dynamic Time Warping (DTW). Jurnal Teknologi Informasi dan Ilmu Komputer, 7(1), 145-154.

W. Li, R. He, B. Liang, F. Yang and S. Han, "Similarity Measure of Time Series With Different Sampling Frequencies Based on Context Density Consistency and Dynamic Time Warping," in IEEE Signal Processing Letters, vol. 30, pp. 1417-1421, 2023, doi: 10.1109/LSP.2023.3316010.

Y. Hwang and S. B. Gelfand, "Fast Sparse Dynamic Time Warping," 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 3872-3877, doi: 10.1109/ICPR56361.2022.9956686.

Y. -J. Liu and Y. -H. Cheng, "Using Dynamic Time Warping Method for the Similarity Measurement of Fluorescent Lamp Arc," 2021 IEEE/ACIS 22nd International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), Taichung, Taiwan, 2021, pp. 162-166, doi: 10.1109/SNPD51163.2021.9704925.




DOI: http://dx.doi.org/10.32497/orbith.v20i1.5432

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License. View My Stats