COSMOS: Computational Shaping and Modeling of Musical Structures

TY - JOUR

T1 - COSMOS: Computational Shaping and Modeling of Musical Structures

AU - Chew, Elaine

N1 - Funding Information: This position paper describes the aims and scope of COSMOS—Computational Shaping and Modeling of Musical Structures—a 5-year research project funded by the European Research Council (ERC). The project aims to study musical structures as they are created in performance and in cardiac arrhythmias using data science, citizen science, and optimization. Like in Widmer ()'s manifesto, the problems identified here and their scope are far greater than can be tackled by one project alone. Thus, this is also a call to the community to join in the efforts to tackle these research issues. Funding Information: EC was supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation programme (Grant Agreement No. 788960). Publisher Copyright: Copyright © 2022 Chew.

PY - 2022/5/27

Y1 - 2022/5/27

N2 - This position paper makes the case for an innovative, multi-disciplinary methodological approach to advance knowledge on the nature and work of music performance, driven by a novel experiential perspective, that also benefits analysis of electrocardiographic sequences. Music performance is considered by many to be one of the most breathtaking feats of human intelligence. It is well accepted that music performance is a creative act, but the nature of its work remains elusive. Taking the view of performance as an act of creative problem solving, ideas in citizen science and data science, optimization, and computational thinking provide means through which to deconstruct the process of music performance in scalable ways. The method tackles music expression's lack of notation-based data by leveraging listeners' perception and experience of the structures elicited by the performer, with implications for data collection and processing. The tools offer ways to parse a musical sequence into coherent structures, to design a performance, and to explore the space of possible interpretations of the musical sequence. These ideas and tools can be applied to other music-like sequences such as electrocardiographic recordings of arrhythmias (abnormal heart rhythms). Leveraging musical thinking and computational approaches to performance analysis, variations in expressions of cardiac arrhythmias can be more finely characterized, with implications for tailoring therapies and stratifying heart rhythm disorders.

AB - This position paper makes the case for an innovative, multi-disciplinary methodological approach to advance knowledge on the nature and work of music performance, driven by a novel experiential perspective, that also benefits analysis of electrocardiographic sequences. Music performance is considered by many to be one of the most breathtaking feats of human intelligence. It is well accepted that music performance is a creative act, but the nature of its work remains elusive. Taking the view of performance as an act of creative problem solving, ideas in citizen science and data science, optimization, and computational thinking provide means through which to deconstruct the process of music performance in scalable ways. The method tackles music expression's lack of notation-based data by leveraging listeners' perception and experience of the structures elicited by the performer, with implications for data collection and processing. The tools offer ways to parse a musical sequence into coherent structures, to design a performance, and to explore the space of possible interpretations of the musical sequence. These ideas and tools can be applied to other music-like sequences such as electrocardiographic recordings of arrhythmias (abnormal heart rhythms). Leveraging musical thinking and computational approaches to performance analysis, variations in expressions of cardiac arrhythmias can be more finely characterized, with implications for tailoring therapies and stratifying heart rhythm disorders.

KW - music performance

KW - computational modeling

KW - technology

KW - citizen science

KW - musical structure

KW - cardiac arrhythmia

KW - data science

KW - computational thinking

UR - https://www.frontiersin.org/articles/10.3389/fpsyg.2022.527539/full

UR - http://www.scopus.com/inward/record.url?scp=85132747380&partnerID=8YFLogxK

U2 - 10.3389/fpsyg.2022.527539

DO - 10.3389/fpsyg.2022.527539

M3 - Article

SN - 1664-1078

VL - 13

JO - Frontiers in Psychology

JF - Frontiers in Psychology

M1 - 527539

ER -