Browse > Article
http://dx.doi.org/10.9717/kmms.2016.19.5.826

Design of Music Learning Assistant Based on Audio Music and Music Score Recognition  

Mulyadi, Ahmad Wisnu (Dept. of IT Convergence and Applications Engineering, Pukyong National University)
Machbub, Carmadi (School of Electrical Engineering and Informatics, Bandung Institute of Technology)
Prihatmanto, Ary S. (Center of ICT Research, Bandung Institute of Technology)
Sin, Bong-Kee (Dept. of IT Convergence and Applications Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.5, 2016 , pp. 826-836 More about this Journal
Abstract
Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.
Keywords
Hidden Markov Model; Support Vector Machine; Chroma Feature; Histogram of Oriented Gradients;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 P. Cano, A. Loscos, and J. Bonada, "Score-Performance Matching Using HMMs," Proceeding of the International Computer Music Association, pp. 441-444, 1995.
2 N. Orio, "An Automatic Accompanist Based on Hidden Markov Models," Proceeding of Advances in Artificial Intelligence, pp. 64-69, 2001.
3 A. Cont, D. Schwarz, and N. Schnell, "Training IRCAM's Score Follower," Proceeding of International Conference on Acoustics, Speech, and Signal Processing, pp. 253-256, 2005.
4 B. Pardo and W. Birmingham, "Modeling Form for On-line Following of Musical Performances," Proceedings of the 20th National Conference on Artificial Intelligence Vol. 2, pp. 1018-1023, 2005.
5 A. Sheh and D. Ellis, "Chord Segmentation and Recognition using EM-Trained Hidden Markov Models," Proceeding of 4th International Symposium on Music Information Retrieval, pp. 185-191, 2003.
6 T. Cho and J.P. Bello, "Real-Time Implementation of HMM-Based Chord Estimation in Musical Audio," Proceeding of the International Computer Music Conference, pp. 16-21, 2009.
7 A. Rabelo, I. Fujinaga, F. Paskiewicz, A.R.S. Marcal, C. Guedes, and J.S. Cardoso, “Optical Music Recognition : State-of-the-art and Open Issues,” International Journal of Multimedia Information Retrieval, Vol. 1, No. 3, pp. 173-190, 2012.   DOI
8 N. Dalal and B. Triggs. “Histograms of Oriented Gradients for Human Detection,” IEEE Computer Vision and Pattern Recognition, Vol. 1, pp. 886-893, 2005.
9 R. Ebrahumzadeh and M. Jampour, “Efficient Handwritten Digit Recognition Based on Histogram,” International Journal of Computer Applications, Vol. 104, No. 9, pp. 10-14, 2014.   DOI
10 R. Ebrahumzadeh and M. Jampour, "Digit Classification Using HOG Features," in MathWorksTM Documentation. http://www.mathworks.com/help/vision/examples/digit-classification-using-hog-features.html. (accessed on June 2, 2016)
11 J.P. Bello, "Chroma and Tonality," New York University's Music Information Retrieval Course. http://www.nyu.edu/classes/bello/MIR.html (accessed June 2, 2016).
12 C. Tralie, "Musical Pitches and Chroma Features" in Duke University's Digital Signal Processing Course, http://www.ctralie.com/Teaching/ECE381_DataExpeditions_Lab1 (accessed on June 2, 2016).
13 D, Boswell. "Introduction to Support Vector Machines", 2002. http://dustwell.com/PastWork/IntroToSVM.pdf. (accessed on June 2, 2016).
14 L.R. Rabiner, “A Tutorial on Hidden Markov Modles and Selected Applications in Speech Recognition,” Proceeding of the IEEE, Vol. 77, pp. 257-286, 1989.   DOI
15 T. Hur, H. Cho, G. Nam, J. Lee, S. Lee, S. Park, et al., "A Study on the Implementation of the System of Content-based Retrieval of Music Data," Journal of Korea Multimedia Society, vol. 12, iss. 11, pp. 1581-1592, 2009.
16 M, Nilsson, First Order Hidden Markov Model : Theory and Implementation Issues, Blekinge Institute of Technology Research Report, pp. 9-54, 2005.
17 K. Schutte, "MATLAB and MIDI". http://kenschutte.com/midi (accessed on June 2, 2016).
18 A.W. Mulyadi and B.-K. Sin. "Chroma Based Music Score-Following Using Hidden Markov Model," Proceeding of the Fall Conference Paper on Korean Multimedia Society, 2015.