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http://dx.doi.org/10.7236/JIWIT.2011.11.3.153

An Implementation of Pan-So-Ri Classification Program Using Naive Bayesian Classifier  

Kim, Won-Jong (을지대학교 의료IT마케팅학과)
Lee, Kang-Bok (을지대학교 의료IT마케팅학과)
Kim, Myung-Gwan (을지대학교 의료IT마케팅학과)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.11, no.3, 2011 , pp. 153-159 More about this Journal
Abstract
Pan-So-Ri singing a story as song is one of Korea traditional musics. it divide into two sect(east-sect, west-sect), and it is hard to classify two sect without knowledge about Pan-So-Ri. In this paper, we have propose a Pan-So-Ri classification program using PCD(Pitch Class Distribution) and Naive Bayesian Classifier. Attribute value of classifier is each appearance frequency of pitch. Experiment is conducted two time with different rounding off location of probability value. Better one show correct classification with east-sect 80%, west-sect 97%, and total accuracy of 88%. this result is used our program.
Keywords
Naive Bayesian Classifier; Data Mining; PCD(Pitch Class Distribution); Supervised Learning;
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1 최동현, 판소리 이야기, 도서출판 인동, pp.44-49, 1999.
2 장윤정, 주제중심의 판소리 지도방안 연구, 한국국악교육연구학회 제 3회 정기총회 및 학술세미나, pp. 129-149, 2009.
3 P. Chordia and A. Rae, Real-time Raag Recognition for interactive music, New Interfaces for Musical Expression, 2008.
4 P. Chordia and A. Rae, Raag recognition using pitch-class and pitch-class dyad distributions. In Proceedings of International Conference on Music Information Retrieval, 2007.
5 P, Chordia and A. Rae, Automatic raag Classification of pitch-tracked performances using pitch-class and pitch-class dyad distributions. In Poceedings of International Conference on Music Information Retrieval, 2007.
6 서종문, 김석배, 판소리 '중고제'의 역사적 이해, 국어교육학회, 24권, pp.33-64, 1992.
7 이중훈, 경상도 지방을 중심으로 발달한 동편제 판소리와 송문일가인 국창 송만갑, 한국음반학, 4호, 1994.
8 조한철, 조근식, 나이브 베이지안 분류자와 메세지 규칙을 이용한 스팸메일 필터링 시스템, 한국 정보과학회 봄 학술논문 발표집, 29권, 1호, pp.223-225, 2002.
9 Mitchell Tom, Machine Learning, McGraw-Hill, Chapter 6 : Bayesian Learning, 1997.
10 Jon Kagstrom, Improving Naive Bayesian Spam Filtering, Mittuniversitetet, 2005
11 문현구, Hybrid Naive Bayes HMM 기법을 사용한 텍스트로부터의 감정 분류, 석사 학위논문, 2002.
12 P. Domingos and M. Pazzani. Beyond Independence: Conditions for the Optimality of the simple Bayesian Classfier. In Proc. of the 13th International Conference on Machine Learning in the New Information Age, ECML 2000.
13 Lawrence R. Rabiner, Michael J. Cheng, Aaron E. Rosenberg, Carol A. Mcgonegal, A Comparative Performance Study of Several Pitch Detection Algorithms, IEEE Transactions on Acoustics, Speech, and Signal Processing, VOL. ASSP-24,NO. 5, 1976.
14 Kristoffer Jensen, Declan Murphy, Segmenting Melodies into Notes, In Olsen, S., ed.: Proc. 10th Danish Conf. on Pattern Recognition and Image Analysis, 2001.
15 김형태, 보이스 오디세이, 북로드, pp135-135, 2007.