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abstract

MiLa: An Audiovisual Instrument for Learning the Curwen Hand Signs

Published: 07 May 2016 Publication History

Abstract

We present a novel musical system for learning the Curwen-Kodaly hand signs, which stand for solfege syllables and pitches (do, re, mi, etc.). These hand postures comprise a system of musical notation typically used in sight-singing training to familiarize with patterns of musical tones. Therefore, what concerns us is music literacy as opposed to practice with any particular instrument. Our system uses a Leap motion sensor to recognize the hand signs and produce corresponding user-recorded sounds. For each hand sign, the system also displays a natural 3D scenery, which is intended to match the metaphorical nature of the tone and has a resemblance to the hand posture (e.g. mi is a steady or calm tone, so the hand sign is an open hand with palm downwards and the scenery is a calm but moving ocean).

References

[1]
Alissa N. Antle, Milena Droumeva, and Greg Corness. 2008. Playing with the sound maker: do embodied metaphors help children learn? Proceedings of the 7th international conference on interaction design and children, 178--185.
[2]
Ricardo Cabello. 2015. three.js. GitHub repository. (16 March 2015). Retrieved June 26, 2015 from https://github.com/mrdoob/three.js.
[3]
John Curwen. 1892. The standard course of lessons and exercises in the tonic sol-fa method of teaching music. London.
[4]
Steven M. Demorest. 2001. Building choral excellence. Oxford University Press, Oxford.
[5]
Leap Motion. 2015. Leap Motion, Inc.
[6]
Erzsébet Legányné Hegyi and Zoltan Kodály. 1975. Solfege according to the Kodály-concept. Zoltán Kodály Pedagogical Institute of Music, Kecskemét.
[7]
Rochelle Mann. 1989. Why Should Elementary Students Have All the Fun? Music Educators Journal 76, 1: 39--42.
[8]
Michael Mark and Charles Gary. 2007. A history of American music education. Rowman & Littlefield Education, Lanham, Md.
[9]
Alan C. McClung. 2008. Sight-Singing Scores of High School Choristers with Extensive Training in Movable Solfege Syllables and Curwen Hand Signs. Journal of Research in Music Education 56, 3: 255266.
[10]
MENC: The National Association for Music Education. 1994. The School Music Program: A New Vision. R&L Education.
[11]
Eero P. Simoncelli and Bruno A. Olshausen. 2001. Natural Image Statistics and Neural Representation. Annual Review of Neuroscience, 24: 1193--1216.
[12]
Jonathan Smallwood, John B. Davies, Derek Heim, Frances Finnigan, Megan Sudberry, Rory O'Connor, and Marc Obonsawin. 2004. Subjective experience and the attentional lapse: Task engagement and disengagement during sustained attention. Consciousness and cognition 13, 4: 657--690.
[13]
Lawrence Wheeler, Carl Orff, Zoltan Kodály and Lois Raebeck. 1985. Orff and Kodaly adapted for the elementary school. W.C. Brown, Dubuque, Iowa.
[14]
Chris Wilson. 2015. Pitch Detect. GitHub repository. (9 October 2014). Retrieved June 24, 2015 from https://github.com/cwilso/PitchDetect.

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  1. MiLa: An Audiovisual Instrument for Learning the Curwen Hand Signs

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        cover image ACM Conferences
        CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems
        May 2016
        3954 pages
        ISBN:9781450340823
        DOI:10.1145/2851581
        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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        New York, NY, United States

        Publication History

        Published: 07 May 2016

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        Author Tags

        1. computer graphics
        2. embodied metaphors
        3. music education
        4. musical instrument
        5. posture recognition

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        CHI'16: CHI Conference on Human Factors in Computing Systems
        May 7 - 12, 2016
        California, San Jose, USA

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        CHI EA '16 Paper Acceptance Rate 1,000 of 5,000 submissions, 20%;
        Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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