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Music scale modeling for melody matching
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Source International Multimedia Conference archive
Proceedings of the eleventh ACM international conference on Multimedia table of contents
Berkeley, CA, USA
SESSION: Reception and posters table of contents
Pages: 359 - 362  
Year of Publication: 2003
ISBN:1-58113-722-2
Authors
Yongwei Zhu  Institute for Infocomm Research, Singapore
Mohan Kankanhalli  National University of Singapore, Singapore
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
SIGCOMM: ACM Special Interest Group on Data Communication
ACM: Association for Computing Machinery
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 4,   Downloads (12 Months): 48,   Citation Count: 3
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ABSTRACT

Several time series matching techniques have been proposed for content-based music retrieval. These techniques have shown to be robust and effective for music retrieval by acoustic inputs, such as query-by-humming. However, due to the key transposition issue, all the current methods need to search a large space for the proper key in melody matching. This computation can be prohibitive for a practical music retrieval system with a large database.In this paper, we present a music scale modeling technique for melody matching. The root note of music scale (Major or Minor) of a melody is estimated by fitting the notes to a music scale model. The estimated root note can then be used as the key in melody matching. To the best of our knowledge, this is the first approach that utilizes music scale knowledge for retrieval. In our experiments, 96% of the songs in the database (3000 melodies) can fit into the music scale model. Promising results for query-by-humming retrieval have been obtained by using this novel approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Harrison, M. Contemporary Music Theory: Level One. Hal Leonard. January 1999.
 
2
A. Ghias, J. Logan, and D. Chamberlin. "Query By Humming". Proceedings of ACM Multimedia 95,.
3
4
 
5
T. Nishimura, and etc. "Music Signal Spotting Retrieval by a Humming Query Using Start Frame Feature Dependent Continuous Dynamic Programming", International Symposium on Music Information Retrieval, USA, 2001.
 
6
Y.W. Zhu, M. Kankanhalli, "A Robust Music Retrieval Method for Query-by-Humming", International Conference on Information Technology: Research and Technology, New Jersey, USA, Aug. 10-13, 2003.
 
7
YY. Zhu, D. Shasha, "Query-by-humming: a Time Series Database Approach" ACM SIGMOD/PODS 2003.


Collaborative Colleagues:
Yongwei Zhu: colleagues
Mohan Kankanhalli: colleagues