Recognition of Beam's Music Notation Patterns Using Artificial Neural Networks with The Backpropagation Method

被引：0
作者：
Haumahu J.P. [1 ]
机构：
[1] Program Studi Teknik Informatika, Universitas Trilogi, Jakarta
来源：
Journal of Information Technology Education: Research | 2019年 / 3卷 / 01期
关键词：
artificial neural network; backpropagation; music notation; pattern recognition;
D O I：
10.31289/JITE.V3I1.2557
中图分类号：
学科分类号：
摘要：
The beam notations is officially used as the standard of international music notation, and is often found in scores for both musical instruments and vocals. In Indonesia, the use of numerical notation is more widely used and understood, because the learning process of notation beams is not easy, and takes time for the introduction of each symbol and its meaning. The pattern recognition technology makes it possible to recognize the pattern of the beam notations. The software used for system development is Matlab, utilizing artificial neural network using backpropagation method to recognize the pattern of beam notation.Backpropagation is a supervised learning method, where the system will be given the training first, and then the system can understand and identify patterns based on the knowledge gained. The final result shows that the system is able to recognize patterns from notations that have been previously studied with the highest percentage of 91.20% © 2019, Journal of Information Technology Education: Research. All Rights Reserved.
引用
收藏
页码：41 / 47
页数：6
相关论文
共 10 条
  • [1] Aghajari E., Gharpure M., Associate D., Incorporating FCM and Back Propagation Neural Network for Image Segmentation, International Journal of Computer &, 4, pp. 121-126, (2011)
  • [2] Asriani F., Nugraha A. W. W., Pengenalan Pola Aksara Jawa Tulisan Tangan dengan Jaringan Syaraf Tiruan Perambatan-Balik, Dinamika Rekayasa, 5, 2, pp. 34-36, (2009)
  • [3] Dongare A. D., Kharde R. R., Kachare A. D., Introduction to Artificial Neural Network, International Journal of Engineering and Innovative Technology (IJEIT), 2, 1, pp. 189-194, (2012)
  • [4] Pengenalan Pola Sinyal Suara Manusia Menggunakan Metode Back Propagation Neural Network, Jurnal EduMatSains, 2, 1, pp. 1-16, (2017)
  • [5] Hidayatno A., Isnanto R. R., Kurnia D., Buana W., Elektro J. T., Teknik F., Diponegoro U., IDENTIFIKASI TANDA-TANGAN MENGGUNAKAN JARINGAN SARAF TIRUAN PERAMBATAN-BALIK (BACKPROPAGATION) Achmad, Jurnal Teknologi, 1, 2, pp. 100-106, (2008)
  • [6] Kishore R., Kaur T., Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition, International Journal of Scientific & Engineering Research, 3, 6, pp. 6-9, (2012)
  • [7] Luhing M. Z., Suryaningrum K. M., Pengenalan Karakter Huruf Rusia dengan Algoritma Perceptron, Processor, 13, 1, pp. 1160-1172, (2018)
  • [8] Pujiyanta A., PENGENALAN CITRA OBJEK SEDERHANA DENGAN JARINGAN SARAF TIRUAN METODE PERCEPTRON, Jurnal Informatika, 3, 1, pp. 268-277, (2009)
  • [9] Suteja B. R., Penerapan Jaringan Saraf Tiruan Propagasi Balik Studi Kasus Pengenalan Jenis Kopi, Jurnal Informatika, 3, 1, pp. 49-62, (2007)
  • [10] Wuryandari M. D., Afrianto I., Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation dan Learning Vector Quantization pada Pengenalan Wajah, Jurnal Komputer Dan Informatika (KOMPUTA), 1, 1, pp. 45-51, (2012)
1