Data-Driven Analysis of Tiny Touchscreen Performance with MicroJam

被引:0
|
作者
Martin, Charles Patrick [1 ]
Torresen, Jim [2 ]
机构
[1] Australian Natl Univ, Res Sch Comp Sci, Canberra, ACT 2601, Australia
[2] Univ Oslo, Dept Informat, Postboks 1080, N-0316 Oslo, Norway
关键词
D O I
10.1162/COMJ_a_00536
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The widespread adoption of mobile devices, such as smartphones and tablets, has made touchscreens a common interface for musical performance. Although new mobile music instruments have been investigated from design and user experience perspectives, there has been little examination of the performers' musical output. In this work, we introduce a constrained touchscreen performance app, MicroJam, designed to enable collaboration between performers, and engage in a data-driven analysis of more than 1,600 performances using the app. MicroJam constrains performances to five seconds, and emphasizes frequent and casual music-making through a social media-inspired interface. Performers collaborate by replying to performances, adding new musical layers that are played back at the same time. Our analysis shows that users tend to focus on the center and diagonals of the touchscreen area, and that they tend to swirl or swipe rather than tap. We also observe that, whereas long swipes dominate the visual appearance of performances, the majority of interactions are short with limited expressive possibilities. Our findings enhance our understanding of how users perform in touchscreen apps and could be applied in future app designs for social musical interaction.
引用
收藏
页码:41 / 57
页数:17
相关论文
共 50 条
  • [1] Data-driven analysis of tiny touchscreen performance with MicroJam
    Martin C.P.
    Torresen J.
    [J]. Computer Music Journal, 2020, 43 (04) : 41 - 57
  • [2] PERFORMANCE ANALYSIS OF DATA-DRIVEN NETWORKS
    OLSDER, GJ
    [J]. SYSTOLIC ARRAY PROCESSORS, 1989, : 33 - 41
  • [3] Data-Driven Performance Analysis of Scheduled Processes
    Senderovich, Arik
    Rogge-Solti, Andreas
    Gal, Avigdor
    Mendling, Jan
    Mandelbaum, Avishai
    Kadish, Sarah
    Bunnell, Craig A.
    [J]. BUSINESS PROCESS MANAGEMENT, BPM 2015, 2015, 9253 : 35 - 52
  • [4] Data-Driven Photovoltaic Module Performance Analysis with FAIR Data
    Li, Mengjie
    Kaltenbaugh, Jarod
    Colvin, Dylan J.
    Oltjen, William C.
    Nihar, Arafath
    Yao, Dominique Akissi
    Yu, Xuanji
    Sehirlioglu, Alp
    French, Roger H.
    Davis, Kristopher O.
    [J]. 2023 IEEE 50TH PHOTOVOLTAIC SPECIALISTS CONFERENCE, PVSC, 2023,
  • [5] Performance analysis in emergency departments: a data-driven approach
    Stefanini, Alessandro
    Aloini, Davide
    Benevento, Elisabetta
    Dulmin, Riccardo
    Mininno, Valeria
    [J]. MEASURING BUSINESS EXCELLENCE, 2018, 22 (02) : 130 - 145
  • [6] Performance analysis in emergency departments: a data-driven approach
    Stefanini, Alessandro
    Aloini, Davide
    Benevento, Elisabetta
    Dulmin, Riccardo
    Mininno, Valeria
    [J]. IFKAD 2017: 12TH INTERNATIONAL FORUM ON KNOWLEDGE ASSET DYNAMICS: KNOWLEDGE MANAGEMENT IN THE 21ST CENTURY: RESILIENCE, CREATIVITY AND CO-CREATION, 2017, : 73 - 84
  • [7] Statistical Performance Analysis of Data-Driven Neural Models
    Freestone, Dean R.
    Layton, Kelvin J.
    Kuhlmann, Levin
    Cook, Mark J.
    [J]. INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 2017, 27 (01)
  • [8] Performance analysis of data-driven pipelined computer architectures
    Chi, Kuang-Hwei
    Tseng, Chien-Chao
    Lin, Chih-Zong
    Chou, Wen-Kuang
    [J]. International Journal of Modelling and Simulation, 2000, 20 (03): : 236 - 247
  • [9] Impact of Practical Skills on Academic Performance: A Data-Driven Analysis
    Rahman, Md. Mostafizer
    Watanobe, Yutaka
    Kiran, Rage Uday
    Thang, Truong Cong
    Paik, Incheon
    [J]. IEEE ACCESS, 2021, 9 : 139975 - 139993
  • [10] Depth analysis of battery performance based on a data-driven approach
    Zhang, Zhen
    Sun, Hongrui
    Sun, Hui
    [J]. ELECTROCHIMICA ACTA, 2024, 474