Klasik Türk Müziğinde Makam Tanıma İçin Veri Madenciliği Kullanımı

Yıl 2018, Cilt: 6 Sayı: 3, 377 - 393, 01.09.2018

Övünç Öztürk , Didem Abıdın Tuğba Özacar

https://doi.org/10.15317/Scitech.2018.139

Öz

Türk Müziği eserleri veri kümeleri hesaplamalı müzik alanında başta makam tanıma çalışmaları olmak üzere çeşitli araştırmalarda kullanılmaktadır. Türk Müziği eserleri, farklı makamsal özellikler göstermeleri bakımından araştırmacılara zengin bir içerik sunmaktadır. Bu alanda en çok referans gösterilen Türk Müziği veri setlerinden biri SymbTr veri setidir. Bu çalışmada, SymbTr veri kümesinden 13 makama ait eserler üzerinde 10 farklı makine öğrenmesi algoritması çalıştırılmış ve bu algoritmaların performansları değerlendirilmiştir. Bu algoritmalar WEKA uygulama ortamı üzerinde çalıştırılarak makam tanımadaki başarım yüzdeleri f-ölçütü ve duyarlılık metrikleri üzerinden hesaplanmıştır. Makine öğrenmesi algoritmaları, %82-%88 arası performans göstermiştir.

Anahtar Kelimeler

Makine öğrenmesi algoritmaları, Makam tanıma, SymbTr, WEKA

USING CLASSIFICATION ALGORITHMS FOR TURKISH MUSIC MAKAM RECOGNITION

Öz

Turkish Music pieces are used in various studies including makam recognition in computational music domain. Turkish Music pieces offer a rich content to the researchers because of their different makam properties. SymbTr is one of the most referred Turkish Music data sets in this area. In this study, the pieces from SymbTr data set belonging to 13 makams are used to execute 10 different machine learning algorithms for makam recognition and the performances of these algorithms are evaluated. These algorithms were executed on WEKA application environment and the performances in makam recognition were obtained with F-measure and recall metrics. The machine learning algorithms performed between 82% and 88%.

Anahtar Kelimeler

Machine learning algorithms, Makam recognition, SymbTr, WEKA

Kaynakça

• Abdoli, S., 2011, "Iranian Traditional Music Dastgah Classification", 12. International Society for Music Information Retrieval Conference (ISMIR 2011), Florida, USA, 275-280, 2011.
• Abidin, D., Öztürk, Ö., Özacar Öztürk, T., 2017, "Klasik Türk Müziğinde Makam Tanıma Için Veri Madenciliği Kullanımı", Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, Vol. 32(4) , pp. 1221-1232.
• Alfaro, E., Gamez, M., Garcia, N., 2013, "adabag: An R Package for Classification with Boosting and Bagging", Journal of Statistical Software, Vol. 54 (2), pp. 1 - 35.
• Bayes, M., Price, M., 1763, "An Essay Towards Solving a Problem in The Doctrine of Chances. By the Late Rev. Mr. Bayes, F. R. S. Communicated by Mr. Price, in a Letter to John Canton, A. M. F. R. S.", Philosophical Transactions, Vol. 53, pp. 370–418.
• Bishop C.M., 2006, Pattern Recognition and Machine Learning, Springer-Verlag.
• Bozkurt, B., Ayangil, R., Holzapfel, A., 2014, “Computational Analysis of Turkish Makam Music: Review of State-of-the-Art and Challenges”, Journal of New Music Research, Vol. 43(1), pp. 3-23.
• Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., 1984, Classification and Regression Trees, Belmont Wadsworth International Group, CA.
• Breiman, L., 1996, "Bagging Predictors" Machine Learning, Vol. 24(2), pp. 123–140.
• Breiman, L., 2001, "Random Forests". Machine Learning, 5–32. doi: 10.1023/A:1010933404324.
• Can, M.C., 2002, "Geleneksel Türk Sanat Müziğinde Arel - Ezgi - Uzdilek Ses Sistemi ve Uygulamada Kullanılmayan Bazı Perdeler", G.Ü. Gazi Eğitim Fakültesi Dergisi, Vol. 22(1), pp. 175-181.
• Caudill, M., 1989, "Neural Network Primer: Part I", AI Expert.
• Cohen, W.W., 1995, "Fast Effective Rule Induction", Proceedings of the 12th International Conference on Machine Learning, 115–123.
• Cortes, C., Vapnik, V., 1995, "Support Vector Networks", Machine Learning, Vol. 20, pp. 273-297.
• Darabi, N., Azimi, N., Nojumi, H., 2006, "Recognition of Dastgah and Makam for Persian Music with Detecting Skeletal Melodic Models", 2. IEEE BENELUX/DSP Valley Signal Processing Symposium,
• Doetsch, P., Buck, C., Golik, P., Hoppe, N., Kramp, M., Laudenberg, J., Oberdörfer, C., Steingrube, P., Forster, J., Mauser, A., “Logistic Model Trees with AUC Split Criterion for the KDD Cup 2009 Small Challenge”, ACM Knowledge Discovery and Data Mining (KDD), France, 77-88, 28 June 2009.
• Duda, R.O., Hart, P.E., Stork, D., 2000, Pattern Classification, Wiley and Sons.
• Fontana, F.A., Mäntylä, M.V., Zanoni, M., Marino, A., 2016, "Comparing and Experimenting Machine Learning Techniques for Code Smell Detection", Empirical Software Engineering, Vol. 21, pp. 1143-1191.
• Frank, E., Witten, I.H., 2000, Data Mining, Morgan Kaufmann Publishers.
• Friedman, N., Geiger, D., Goldszmidt, M., 1997, "Bayesian Network Classifiers", Machine Learning, Vol. 29, pp. 131–163.
• Gedik, A.C., Bozkurt, B., 2008, "Automatic Classification of Turkish traditional Art Music Recordings by Arel Theory", Conference on Interdisciplinary Musicology, Thessaloniki, Greece.
• Gedik, A.C., Bozkurt, B., 2010, "Pitch-Frequency Histogram-Based Music Information Retrieval for Turkish Music", Signal Processing, Vol. 90, pp. 1049-1063, Elsevier.
• Greene, W.H., 2012, Econometric Analysis (Seventh ed.). Boston: Pearson Education, 803–806.
• Ho, T.K., 1995, "Random Decision Forests", Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 278-282.
• Jurafsky, D., Martin, J.H., 2014, N-Grams, Speech and Language Processing, Pearson Education, pp. 93-136.
• Kalaycı, I., Korukoğlu, S., 2012, "Classificatıon of Turkish Maqam Music using K-Means Algorithm and Artificial Neural Networks (in Turkish)", Proc. 20th IEEE Signal Processing and Communications Applications Conference (SIU), Muğla, Türkiye.
• Kalender, N., Ceylan, M., Karakaya, O., 2012, “Türk Müziği Makamlarının Sınıflandırılması için Yeni Bir Yaklaşım: Kombine YSA”, ASYU 2012 Akıllı Sistemler Yenilikler ve Uygulamaları Symposium, Trabzon, Türkiye.
• Karaosmanoğlu, M.K., 2012, "A Turkish Makam Music Symbolic Database for Music Information Retrieval: SymbTr", 13. International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal, 223-228.
• Karray, F.O., Silva, C.D., 2004, Soft Computing and Intelligent Systems Design: Theory, Tools and Applications, Addison Wesley Pearson Press, New York, USA.
• Kizrak, M.A., Bayram, K.S., Bolat, B., 2014, “Classification of Classic Turkish Music Makams”, Innovations in Intelligent Systems and Applications (INISTA 2014), Alberobello, Italy, 394-397, 23-25 June 2014.
• Kizrak, M.A., Bolat, B., 2014, “Klasik Türk Müziği Makamlarının Tanınması”, Akıllı Sistemlerde Yenilikler ve Uygulamaları Sempozyumu (ASYU 2014), Katip Çelebi Üniversitesi, İzmir, Türkiye.
• Kizrak, M.A., Bolat, B., 2015,"Classification of Turkish Music Makams bu Using Deep Belief Networks", IEEE 23. Sinyal İşleme ve İletişim Uygulamaları Kurultayı, Malatya, Türkiye, 2015.
• Landwehr, N., Hall, M. & Frank, E., 2005, "Logistic Model Trees" Machine Learning, Vol. 59, pp. 161. Meenakshi, Geetika, 2014, "Survey on Classification Methods using WEKA", International Journal of Computer Applications, Vol. 86(18), pp. 16-19.
• Mehdiyev, N., Krumeich, J., Enke, D., Werth, D., Loos, P., 2015, "Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques", Procedia Computer Science, Vol. 61, pp. 395-401.
• Moore, A., Lee, M.S., 1998, “Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets”, Journal of Artificial Intelligence Research (JAIR), Vol. 8, pp. 67-91.
• Özkan, İ.H., 1994, Türk Musikisi Nazariyatı ve Usulleri Kudüm Velveleleri, Ötüken Neşriyat, İstanbul, 57-58.
• Pearl, J., 1988, Probabilistic Reasoning in Intelligent Systems, San Francisco, CA, Morgan Kaufmann.
• Platt, J.C., 1998, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines", Technical Report MSR-TR-98-14, Microsoft Research.
• Powers, D.M.W., 2011, "Evaluation: From Precision, Recall and F-measure to ROC, Informedness, Markedness & Correlation", Journal of Machine Learning Technologies, Vol. 2, pp. 37-63.
• Quinlan, J.R., 1986, "Induction of Decision Trees", Machine Learning 1, 81-106.
• Quinlan, J.R., 1993, C4.5 Programs for Machine Learning, Morgan Kaufmann Publishers.
• Rojas,R., 1996, Neural Networks: A Systematic Introduction, Springer-Verlag, Berlin.
• Russell, S., Norvig, P, 2010, Artificial Intelligence, A Modern Approach, 3rd Edition, Prentice Hall, 511-512.
• Salama, A.A., Eisa, M., ELhafeez, S.A., Lotfy, M.M., 2015, “Review of Recommender Systems Algorithms Utilized in Social Networks based e-Learning Systems & Neutrosophic System”, Neutrosophic Sets and Systems, Vol. 8, pp. 33.
• Snyman, J., 2005, Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms, 97, XX-258.
• Şentürk, S., Gulati, S., Serra, X., 2013, "Score Informed Tonic Identification for Makam Music of Turkey", 14. International Society for Music Information Retrieval Conference (ISMIR 2013), Curitiba, Brasil.
• Şentürk, S., Holzapfel, A., Serra, X., 2012, "An Approach for Linking Score and Audio Recordings in Makam Music of Turkey", 2. CompMusic Workshop, İstanbul, Turkey, 95-106.
• Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I., 2016, "Spatial Prediction Models for Shallow Landslide Hazards: a Comparative Assessment of the Efficacy of Support Vector Machines, Artificial Neural Networks, Kernel Logistic Regression and Logistic Model Tree", Landslides, Vol. 13 (2), pp. 361–378.
• Ünal, E., Bozkurt, B., Karaosmanoğlu, M.K., 2012, "N-Gram Based Statistical Makam Detection on Makam Music in Turkey Using Symbolic Data", 13. International Society for Music Information Retrieval Conference (ISMIR 2012), Porto, Portugal.
• Wang, Z., Xue, X., 2014, Multi-class Support Vector Machine, Chapter 2, Springer International Publishing, 23-24.
• Witten, I.H., Frank, E., 2005, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Press, San Francisco, USA.
• Wu, X., et al., 2008, "Top 10 Algorithms in Data Mining", Knowledge and Information Systems, Vol. 14, pp. 1-37.