Crowd-driven Music: Interactive and Generative Approaches using Machine Vision and Manhattan

Nash, Chris

Abstract

This paper details technologies and artistic approaches to crowd-driven music, discussed in the context of a live public installation in which activity in a public space (e.g. a busy railway platform) is used to drive the automated composition and performance of music. The approach presented uses realtime machine vision applied to a live video feed of a scene, from which detected objects and people are fed into Manhattan (Nash, 2014), a digital music notation that integrates sequencing and programming to support the live creation of complex musical works that combine static, algorithmic, and interactive elements. The paper discusses the technical details of the system and artistic development of specific musical works, introducing novel techniques to mapping chaotic systems to musical expression and exploring issues of agency, aesthetic, accessibility, and adaptability relating to composing interactive music for crowds and public spaces. In particular, performances as part of an installation for BBC Music Day 2018 are described. The paper subsequently details a practical workshop in crowd-driven music, delivered digitally, exploring the development of interactive performances in which the audience or general public actively or passively control live generation of a musical piece. Exercises support discussions on technical, aesthetic, and ontological issues arising from the identification and mapping of structure, order, and meaning in non-musical domains to analogous concepts in musical expression. Materials for the workshop are available freely within the Manhattan software.

Citation

Nash, C. (in press). Crowd-driven Music: Interactive and Generative Approaches using Machine Vision and Manhattan

• Conference Name: New Interfaces for Musical Expression
• Start Date: Jul 27, 2020
• Acceptance Date: Mar 30, 2020
• Online Publication Date: Jul 27, 2020
• Deposit Date: Jun 24, 2020
• Publicly Available Date: Sep 28, 2020
• Keywords: Author Keywords crowd-driven music, audience participation, open works, interactive art, generative scores, machine learning, machine vision CCS Concepts • Applied computing → Sound and music computing; Performing arts; • Human-centered computing → Mixed
• Public URL: https://uwe-repository.worktribe.com/output/6052512