all 15 to 16 years old, from an advanced music class. Students that had a reasonable level of sight reading ablity were invited to join the evaluation by their teacher. The experiment consisted of thirty short eight-bar exercises, of which fifteen where of the large size and fifteen of the small size. Each size category had a set of exercises that ranged in difficulty and contained either pitch or rhythmic errors. At the end of the set of exercises the participants filled out a questionare indicating how hard they found the two differing sizes were to read from, and which one they would prefer to learn or perform from. They were also given the opportunity to comment on either size, the experiment and computers in music in general. For each task, the participant can either make a correct error identification, miss observing an error (false negative), or incorrectly find an error (false positive). Overall we found that the number of correct identifications of pitch errors (average 11.8 out of 15) was statistically significantly worse (p < 0.002) than those for rhythm errors (average 14.6 out of 15). However, there was no significant difference between the number of correct error identifications between the large and small score sizes - although participants performed marginally worse on the smaller scores on average. For false positives (detecting a non-existent error) there was also no significant difference, although the average for the smaller scores was marginally higher. A similar effect was observed for false negatives for rhythm. From this we can conclude that relatively small scores (about 75% of the size of normal printed scores) can be read reasonably accurately on a computer screen. However, the results from the questionnaire show a marked preference for the larger sized score as it was the easist to read. The preference of size, in the area of difficulty in readablity, showed a significantly higher result for the larger images, giving a reliable result (Wilcoxon Signed Ranks Test, Z[15] = 3.38, p < 0.000363). Thus, while the participants can cope with small music surprisingly well, it can require more concentration and all else being equal, they would prefer a "normal" size. For a digital music stand this bodes well, as smaller images can be used, for example, when the performer is familiar with the music and only needs to refer to it occasionally. 4. CONCLUSION Our experiments have explored the two conflicting goals of minimising page turning effort and maximising the legibility and readability of images on a digital music stand. The page turning evaluation study carried out gave some insight into determining musicians' preferences for various methods of page turning. An interesting result of this study showed that scrolling is the hardest page turning method to use on a digital music stand, which highlights how unusual this application is since scrolling is very common on GUI editing systems, including notation editors. There seems to be a preference for the musician to be in control of when changes occur, and so a simple foot pedal or button is likely to be suitable for most needs. The experiment to determine how well students could read large and small scores on a computer screen found only insignificant differences between presenting music in two different sizes, which were (respectively) smaller and larger than conventional paper music. However, they did express a significant preference for the larger music. In practice there is a useful tradeoff available between the size of the image that is displayed and the frequency with which page turns must be made. Even fairly small displays can be acceptable for some situations, and it is likely that in general musicians will use enlarged displays with frequent page turns for practising, and as they become more familiar with the music, move to a smaller image with fewer page turns which is more suitable for a performance. 5. REFERENCES [1] V. Barnett. Sample Survey principles and methods. Hodder, 1991. [2] P. Bellini, P. Nesi, and M.B. Spinuc. Cooperative visual manipulation. of music notation. ACM Transactions on Computer-Human Interaction (TOCHI), 9(3):194-237, September 2002. [3] Pierfrancesco Bellini, Fabrizio Fioravanti, and Paolo Nesi. Managing music in orchestras. IEEE Computer, 32(9):26 - 34, September 1999. [4] S. Furneaux and M. F. Land. The effects of skill on the eye-hand span during musical sight-reading. Proceeding of the Royal Society of London Series B, 266(1436):2435-2440, 1999. [5] C. Graefe, D. Wahila, J. Maguire, and 0. Dasna. Designing the Muse: A digital music stand for the symphony musician. In Conference proceedings on Human factors in computing systems, pages 436-441, April 1996. [6] Juichi Kosakay, Miyuki Miyazawa, and Masumi Kizaki. Research and result of a performerfriendly electronic music stand. Technical Report 106, IPSJ SIGNotes Human Interface, 2003. http://www.ipsj.or.jp/members/SIGNotes/Eng/1 1/2003/106/index.html. [7] John McPherson. Page Turning -Score Automation for Musicians. Honours report, University of Canterbury, 1999. http://www.cosc.canterbury.ac.nz/research/reportslHonsReps/1999/honsD905.pdf. [8] Richard Picking. Reading music from screens vs paper. Behaviour and Information Technology, 16(2):72-78, 1997. [9] John A. Sloboda. The Musical Mind. Oxford University Press, 1985.
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