Journal of Multimedia Information System

Korea Multimedia Society (한국멀티미디어학회)
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How Query by humming, a Music Information Retrieval System, is Being Used in the Music Education Classroom

  • Received : 2017.08.13
  • Accepted : 2017.09.14
  • Published : 2017.09.30
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Abstract

This study does a qualitative and quantitative analysis of how music by humming is being used by music educators in the classroom. Music by humming is part division of music information retrieval. In order to define what a music information retrieval system is first I need to define what it is. Berger and Lafferty (1999) define information retrieval as "someone doing a query to a retrieval system, a user begins with an information need. This need is an ideal document- perfect fit for the user, but almost certainly not present in the retrieval system's collection of documents. From this ideal document, the user selects a group of identifying terms. In the context of traditional IR, one could view this group of terms as akin to expanded query." Music Information Retrieval has its background in information systems, data mining, intelligent systems, library science, music history and music theory. Three rounds of surveys using question pro where completed. The study found that there were variances in knowledge, training and level of awareness of query by humming, music information retrieval systems. Those variance relationships where based on music specialty, level that they teach, and age of the respondents.

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References

  1. M. Barthet, G. Fazekas, S. Dixon, S, and M. Sandler, Social Media Retrieval for Music Education, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.414.9076&rep=rep1&type=pdf, Mar 3, 2015.
  2. E. Benetos, S. Dixon, D. Giannoulis, H. Kirchhoff, and A. Klapuri, "Automatic music transcription: challenges and future directions," Journal of Intelligent Information Systems, Vol. 41, No.3, pp.407, 2013.
  3. A. Berger and J. Laerty, Information Retrieval as Statistical Translation, http://www.mti.ugm.ac.id/-adji/courses/resources/doctor/scholargoogle/StatisticalTranslation99.pdf, October, 2014.
  4. A. Bryman, Social Research Methods, New York: Oxford University Press, 2012.
  5. J. Bu et el, "Music Recommendation by Unified Hypergraph: Combining Social Media Information and Music Content," http://dl.acm.org/citation.cfm?id=1874005, March 2015.
  6. J. Creswell, "Qualitative & Research Design," Thousand Oaks, California: SAGE., 2013
  7. D. A. Dillman," Mail and Internet surveys: The tailored design method (2nd)," New York, NY: John Wiley, 2000.
  8. E. W. Eisner, "The enlightened eye: Qualitative inquiry and the enhancement of educational practice," New York, NY: Macmillan, 1991.
  9. Steam Boiler Water Level Control System Psychology Essay, http://www.ukessays.com/essays/psychology/steamboiler-water-level-control-system-psychologyessay. php?cref=1, November 2013.
  10. R. Faulkner R, "The value of data mining in music education research and some findingsfrom its application to a study of instrumental learning during childhood,' International Journal of Music Education, Vol. 28, Issue 3, pp. 212-230. 2010. https://doi.org/10.1177/0255761410371048
  11. F R., A STUDENT'S GUIDE TO METHODOLOGY,. Harvard Educational Review, Education Research Complete, Ipswich, MA., 2015.
  12. G. Haus, M. Longari, E. Pollastri, "A Score-Driven Approach to Music Information Retrieval," Journal of the American Society for Information Science and Technology, Vol 12, pp.1045. Oct. 2005.
  13. P. Henley, "Effects of modeling and tempo patterns as practice techniques on theperformance of high school instrumentalists," Journal of Research in Music Education, Vol. 49, p p . 169-180, 2001 https://doi.org/10.2307/3345868
  14. R. J.-S., C.-L. Jang, Hsu, and H.-R. Lee, "Continuous HMM and its enhancement for singing/humming query retrieval", in Proc. 6th International Conference on Music Information Retrieval, 2005.
  15. J.-S. Roger Jang and Hong-Ru Lee, "A General Framework of Progressive Filtering and Its Application toQuery by Singing/Humming", IEEE Transactions on Audio, Speech, and Language Processing, No. 2, Vol. 16, PP. 350-358, Feb 2008. https://doi.org/10.1109/TASL.2007.913035
  16. J. Jang and H. Lee, Hierarchical filtering method for content-based music retrieval via acoustic input, http://dl.acm.org/citation.cfm?id=500201, Oct. 2014.
  17. D. Lane, "Introduction to Linear Regression," http://onlinestatbook.com/2/regression/intro.html, March 13, 2015
  18. Y. S. Lincoln and E. G. Guba, Naturalistic inquiry. Beverly Hills, CA: Sage, 1985.
  19. T. Lone and R. Khan, "Data Mining: Competitive tool to Digital Library", Journal of Library and Information Technology, Vol. 34, No. 5, PP 401-406, 2014. https://doi.org/10.14429/djlit.34.6722
  20. L. Mangan, "What are the challenges and opportunities in Music Information Retrieval for academic music libraries in Ireland, from a librarian and user perspective?" http://esource.dbs.ie/bitstream/handle/10788/515/ma_mangan_l_2012.pdf?sequence=1, October 2014.
  21. D. Muijs, D, "Introduction to quantitative research," http://www.sagepub.com/upm-data/36869_muijs.pdf, March 17, 2015
  22. S. Prichard S, "Listening to Learn: The Status of Listening Activities in Secondary," 2022.
  23. Instrumental Ensemble Classes. Contributions to Music Education, Vol. 39, pp.101-115
  24. Rho Seungmin, "an intelligent mobile music retrieval system," Multimedia Systems. Vol. 17, Issue 4, pp. 313-326. https://doi.org/10.1007/s00530-010-0212-y
  25. M. Saunders and P. Lewis, "Thornhill Adrian," Research methods for business Students.
  26. Bridget Somekh, and Cathy Lewin, "Research methods in the social sciences," Sage, 2005.
  27. J. Sowa, "Architecture for intelligent systems," IBM Systems Journal, Vol. 41, Issue 3, pp.331, 2002. https://doi.org/10.1147/sj.413.0331
  28. G. Tzanetakis, A. Ermolinsky and P. Cook, "Pitch Histograms in Audio and Symbolic Music Information Retrieval," Journal of New Music Research., Vol. 32, Issue 2, pp.143-152, Jun 2003. https://doi.org/10.1076/jnmr.32.2.143.16743
  29. Wiering, "Cognition-based Segmentation for Music Information Retrieval Systems," Journal of New Music Research, Vol. 38, Issue 2, pp.139-154, June 2009. https://doi.org/10.1080/09298210903171145
  30. S. Vandermerwe, From tin soldiers to Russian dolls: Creating added value through services, Butterworth-Heinemann, 1993.
  31. https://www.musicfirst.com/, Feb. 12, 2015.