@inproceedings{bc1f0a517b154fa4b2cfe98fb8735c46,
title = "Playing fast and loose with music recognition",
abstract = "We report lessons from iteratively developing a music recognition system to enable a wide range of musicians to embed musical codes into their typical performance practice. The musician composes fragments of music that can be played back with varying levels of embellishment, disguise and looseness to trigger digital interactions. We collaborated with twenty-three musicians, spanning professionals to amateurs and working with a variety of instruments. We chart the rapid evolution of the system to meet their needs as they strove to integrate music recognition technology into their performance practice, introducing multiple features to enable them to trade-off reliability with musical expression. Collectively, these support the idea of deliberately introducing 'looseness' into interactive systems by addressing the three key challenges of control, feedback and attunement, and highlight the potential role for written notations in other recognition-based systems.",
keywords = "Attunement, Casual interactions, H-metaphor, Looseness, Music recognition, Notation, Performance, Sensing systems",
author = "Chris Greenhalgh and Steve Benford and Adrian Hazzard and Alan Chamberlain",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 2017 ACM SIGCHI Conference on Human Factors in Computing Systems, CHI 2017 ; Conference date: 06-05-2017 Through 11-05-2017",
year = "2017",
month = may,
day = "2",
doi = "10.1145/3025453.3025900",
language = "English",
series = "Conference on Human Factors in Computing Systems - Proceedings",
publisher = "Association for Computing Machinery",
pages = "4302--4313",
booktitle = "CHI 2017 - Proceedings of the 2017 ACM SIGCHI Conference on Human Factors in Computing Systems",
address = "United States of America",
}