A comparative evaluation of search techniques for query-by-humming using the MUSART testbed

Journal of the American Society for Information Science and Technology

Volumn 58, Issue 5, 2007, Pages 687-701

  Dannenberg, Roger B ^a   Birmingham, William P ^b   Pardo, Bryan ^c   Hu, Ning ^d   Meek, Colin ^e   Tzanetakis, George ^f  

^a Carnegie Mellon University   (United States)

^b GROVE CITY COLLEGE   (United States)

^c Northwestern University   (United States)

^d GOOGLE INC   (United States)

^e MICROSOFT   (United States)

^f UNIVERSITY OF VICTORIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONTOUR MATCHING; DATABASE SIZE; MUSICAL TRAINING; SEARCH ALGORITHMS;

ALGORITHMS; COMPUTER MUSIC; DATABASE SYSTEMS; HIDDEN MARKOV MODELS; PATTERN MATCHING; QUERY PROCESSING;

INFORMATION RETRIEVAL SYSTEMS;

EID: 33947362416     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20532     Document Type: Article

References (34)

1. Bainbridge, D., Dewsnip, M., & Witten, I.H. (2002, December). Searching digital music libraries. Paper presented at the Digital Libraries: People, Knowledge, and Technology: 5th International Conference on Asian Digital Libraries, Singapore.
2. Bainbridge, D., Nevill-Manning, C.G., Witten, I.H., Smith, L.A., & McNab, R.J. (1999, August). Towards a digital library of popular music. Paper presented at the International Conference on Digital Libraries, Berkeley, CA.
3. Clausen, M. Englebrecht, R., Meyer, D., Schmitz, J. (2000, October).
4. Proms: A web-based tool for searching in polyphonic music. Paper presented at the First International Symposium on Music Information Retrieval, Flymouth, MA.
5. Dannenberg, R.B., Birmingham, W.P., Tzanetakis, G., Meek, C., Hu, N., & Pardo, B. (2003, October). The MUSART testbed for query-by-humming evaluation. Paper presented at the the ISMIR 2003 Fourth International Conference on Music Information Retrieval, Baltimore, MD.
6. Dannenberg, R.B., & Hu, N. (2004, October). Understanding search performance in query-by-humming systems. Paper presented at the ISMIR 2004 Fifth International Conference on Music Information Retrieval, Barcelona.
7. Doraisamy, S., & Ruger, S. (2002, October). A comparative and fault-tolerance study of the use of n-grams with polyphonic music. Paper presented at the ISMIR 2002 Third International Conference on Music Information Retrieval, Paris, France.
8. Doraisamy, S., & Ruger, S. (2003). Robust polyphonic music retrieval with n-grams. Journal of Intelligent Information Systems, 21(1), 53-70.
9. Downie, J.S., & Nelson, M. (2000). Evaluation of a simple and effective music information retrieval method. Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 73-80). New York: ACM.
10. Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis. Cambridge/New York: Cambridge University Press.
11. Durey, A.S., & Clements, M.A. (2001. October). Melody spotting using hidden Markov Models. Paper presented at the 2nd Annual International Symposium on Music Information Retrieval, Bloomington, IN.
12. Goto, M. (2000, June). A robust predominant-f0 estimation method for real- time detection of melody and bass lines in CD recordings. In the Proceedings of the 2000 IEEE International Conference on Acoustics. Speech and Signal Processing (II, pp. 757-760). Piscataway, NJ: IEEE.
13. Hewlett, W., & Selfridge-Field, E. (Eds.). (1998). Melodic similarity: Concepts, Procedures, and Applications (Vol. 11). Cambridge: MIT Press.
14. Hoos, H., Rentz, K., & Gorg, M. (2001, October). GUIDO/MIR-An experimental musical information retrieval system based on GUIDO music notation. Paper presented at the International Symposium on Music Information Retrieval, Bloomington, IN.
15. Hsu, J.L., & Chen, A.L.P. (2001). Building a platform for performance study of various music information retrieval approaches. Paper presented at the 2nd Annual International Symposium on Music Information Retrieval, Bloomington, IN.
16. Hsu, J.L., Chen, A.L.P., Chen, H.-C., & Liu, N.-H. (2002, November). The effectiveness study of various music information retrieval approaches. Paper presented at the Conference on Information and Knowledge Management, McLean, VA.
17. Hu, N., & Dannenberg, R.B. (2002). A comparison of melodic database retrieval techniques using sung queries. In G. Marchionini & W. Hersh (Eds.), the Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 301-307). New York: ACM.
18. Jin, H., & Jagadish, H. V. (2002, October). Indexing Hidden Markov Models for Music Retrieval. Paper presented at the ISMIR 2002 Conference Proceedings. Paris, France.
19. Mazzoni, D., & Dannenberg, R.B. (2001, October). Melody matching directly from audio. Paper presented at the 2nd Annual International Symposium on Music Information Retrieval. Bloomington, Indiana.
20. McNab, R.J., Smith, L.A., Witten, I.H., Henderson, C.L., & Cunningham, S.J. (1996). Towards the digital music library: Tune retrieval from acoustic input. In E. Fox & G. Morchionini (Eds.), the Proceedings of the first ACM International Conference on Digital Libraries (pp. 11-18). New York: ACM.
21. Meek, C., & Birmingham, W.P. (2001, October). Thematic Extractor. Paper presented at the 2nd Annual International Symposium on Music Information Retrieval, Bloomington, IN.
22. Meek, C., & Birmingham, W.P. (2004). A comprehensive trainable error model for sung music queries. Journal of AI Research, 22, 57-91.
23. Mongeau, M., & Sankoff, D. (1990). Comparison of musical sequences. In W. Hewlett & E. Selfridge-Field (Eds.), Melodic similarity concepts, procedures, and applications (Vol. 11). Cambridge, MA: MIT Press.
24. Pardo, B., & Birmingham, W.P. (2002, October). Encoding Timing Information for Musical Query Matching. Paper presented at the ISMIR 2002 Conference Proceedings, Paris, France.
25. Pardo, B., & Birmingham, W.P. (2002). Improved Score Following for Acoustic Performances. Paper presented at the Proceedings of the 2002 International Computer Music Conference, Gothenburg, Sweden.
26. Pardo, B., Birmingham, W.P., & Shifrin, J. (2004). Name that tune: A pilot studying in finding a melody from a sung query. Journal of the American Society for Information Science and Technology, 55(4), 283-300.
27. Pauws, S. (2002, October). CubyHum: A fully operational query-by-humming system. Paper presented at the ISMIR 2002 Conference Proceedings, Paris, France.
28. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of IEEE, 77(2), 257-286.
29. Salton, G. (1988). Automatic Text Processing: The transformation, analysis, and retrieval of information by computer. Reading, MA: Addison-Wesley.
30. Salton, G., & McGill, M.J. (1983). Introduction to modern information retrieval. New York: McGraw-Hill.
31. Sankoff, D., & Kruskal, J.B. (1983). Time warps, string edits, and macromolecules: The theory and practice of sequence comparison. Reading, MA: Addison-Wesley.
32. Tolonen, T., & Karjalainen, M. (2000). A computationally efficient multi-pitch analysis model. IEEE Transactions on Speech and Audio Processing, 8(6), 708-716.
33. Tseng, Y.H. (1999). Content-based retrieval for music collections. In the Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information (pp. 176-182). New York: ACM.
34. Uitdenbogerd, A., & Zobel, J. (1999). Melodic matching techniques for large music databases. In the Proceedings of the 7th ACM International Multimedia Conference (pp. 57-66). New York: ACM.