SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms

We present an extension of SonOpt, the first ever openly available tool for the sonification of bi-objective population-based optimisation algorithms. SonOpt has already introduced benefits on the understanding of algorithmic behaviour by proposing the use of sound as a medium for the process monitoring of bi-objective optimisation algorithms. The first edition of SonOpt utilised two different sonification paths to provide information on convergence, population diversity, recurrence of objective values across consecutive generations and the shape of the approximation set. The present extension provides further insight through the introduction of a third sonification path, which involves hypervolume contributions to facilitate the understanding of the relative importance of non-dominated solutions. Using a different sound generation approach than the existing ones, this newly proposed sonification path utilizes pitch deviations to highlight the distribution of hypervolume contributions across the approximation set. To demonstrate the benefits of SonOpt we compare the sonic results obtained from two popular population-based multi-objective optimisation algorithms, Non-Dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D), and use a Multi-objective Random Search (MRS) approach as a baseline. The three algorithms are applied to numerous test problems and showcase how sonification can reveal various aspects of the optimisation process that may not be obvious from visualisation alone. SonOpt is available for download at https://github.com/tasos-a/SonOpt-2.0.

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SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

Asonitis

Allmendinger

Benatan³

et al. 2023

Genet Program Evolvable Mach

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Recurring patterns: An ethnographic study on the adoption of AI music tools by practitioners of electroacoustic, contemporary and popular musics

Kehagia,

Moriaty

2023

Journal of Pervasive Media

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The intersection of artificial intelligence (AI) and art has been a topic of great interest in recent times. Driven by greater visibility of accessible AI applications within mainstream media, artists have increased their uptake of such tools as means of exploring and expanding their creative expressions. With the music industry also displaying similar levels of curiosity for AI tools, practitioners and audiences voice diverging opinions on the topics of artistic authenticity, creative labour and the threats posed by thinking machines on the future of musicians’ careers. This article aims to explore these topics through an ethnographic study conducted through interviews with five composers active in the areas of electroacoustic music, contemporary composition and experimental electronic music. The discussions reveal some of the software and methodologies currently popular among composers, the challenges faced and avenues presented when adopting AI tools, as well as the attitudes and discourse that permeate the niche circles of AI-generated music. The findings point towards the swift uptake of new technologies by curious artists and the slow development of trust in AI applications by traditionalist makers and listeners, suggesting a continuation of the patterns of behaviour evident since the emergence of music technology.

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SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms

Cited by 2 publications

References 27 publications

SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound

Recurring patterns: An ethnographic study on the adoption of AI music tools by practitioners of electroacoustic, contemporary and popular musics

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