Applications of binary classification and adaptive boosting to the query-by-humming problem

ISMIR 2005 - 6th International Conference on Music Information Retrieval

Volumn , Issue , 2005, Pages 245-251

  Parker, Charles ^a  

^a OREGON STATE UNIVERSITY   (United States)

Author keywords

Articial intelligence; Melodic retrieval; Sequence alignment

Indexed keywords

ARTICIAL INTELLIGENCE; BINARY CLASSIFICATION; MATCHING TECHNIQUES; MELODIC RETRIEVAL; QUERY-BY-HUMMING; QUERY-BY-HUMMING SYSTEM; RUNNING TIME; SEQUENCE ALIGNMENTS; STATE-OF-THE-ART ALGORITHMS;

ADAPTIVE BOOSTING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; HIDDEN MARKOV MODELS;

INFORMATION RETRIEVAL;

EID: 64849093376     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Stephen Downie and Prof. Michael Nelson. Evaluation of a simple and effective music information retrival method. In Proc. 23rd International ACM SIGIR conference on Research and Development in Information Retrieval, 2000.
2. Bryan Pardo, William Birmingham, and Jonah Shifrin. Name that tune: A pilot study in finding a melody from a sung query. Journal of the American Society for Information Science and Technology, 55(4), 2004.
3. Roger B. Dannenberg, William P. Birmingham, George Tzanetakis, Colin Meek, Ning Hu, and Bryan Pardo. The musart testbed for query-byhumming evaluation. In Proc. 4th International Symposium on Music Information Retrieval, 2003.
4. Naoko Kosugi and Yuichi Nishihara. A practical query-by-humming system for a large music database. In Proc. 8th ACM Multimedia Conference, 2000.
5. Dominic Mazzoni and Roger B. Dannenberg. Melody matching directly from audio. In Proc. 2nd Annual International Symposium on Music Information Retrieval, 2001.
6. Colin Meek and William Birmingham. Johnny can't sing: A comprehensive error model for sung music queries. In Proc. 3rd International Symposium on Music Information Retrieval, 2002.
7. Paul Boersma. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. Proceedings of the Institute of Phonetic Sciences, 17:97-110, 1993.
8. The Digital Tradition Folk Music Database.
9. M. S. Smith and T. F. Waterman. Identification of common molecular subsequence. Journal of Molecular Biology, 147:195-197, 1981.
10. Colin Meek. Modelling error in query-by-humming applications. PhD thesis, The University of Michigan, 2004.
11. Jie Wei. Markov edit distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(3):311-321, March 2004.
12. Marcel Mongeau and David Sankoff. Comparison of musical sequences. Computers and the Humanities, 24:161-175, 1990.
13. Richard Durbin, Sean Eddy, Anders Krogh, and Graeme Mitchison. Biological Sequence Analysis. Cambridge University Press, 1998.
14. Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, chapter 20. Prentice Hall, second edition, 2003.
15. Ioannis Tsochantaridis, Thoman Hofmann, Thorsten Joachims, and Yasemin Altun. Support vector machine learning for interdependent and structured output spaces. In Proc. 21st International Conference on Machine Learning, 2004.
16. Colin Meek and William P. Birmingham. The dangers of parsimony in query-by-humming applications. In Proc. 4th International Symposium on Music Information Retrieval, 2003.
17. Bryan Pardo and William Birmingham. Encoding timing information for musical query matching. In Proc. 3rd International Symposium on Music Information Retrieval, 2002.
18. Robert E. Schapire. The boosting approach to machine learning: An overview. In MSRI Workshop on Nonlinear Estimation and Classification, 2002.
19. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan Kaufmann, San Francisco, 2000.
20. John Lafferty, Andrew McCallum, and Fernando Pereira. Conditional random fields: Probabilistic models for segmenting and lebeling sequence data. In ICML, 2001.
21. Ben Taskar, Carlos Guestrin, and Daphane Koller. Max margin markov networks. In NIPS, 2004.
22. Thomas G. Dietterich. Machine learning for sequential data: A review. Structural, Syntactic, and Statistical Pattern Recognition; Lecture Notes in Computer Science, 2396:15-30, 2002.