Music producers produce, partly, by enabling and making suggestions to musicians and sound engineers; by bringing their attention to artistic and technical problems and expressive potential; and by easing difficulties in creative work. Increasingly, the computational technologies available to musicians and engineers are able to perform related— not identical— functions; for example, recommending technical corrections that improve sound quality or conform to genre-specific norms, or algorithmically adjusting performances. Thereby, technology, especially “smart” technology, provides means to address some needs that producers have traditionally met. But to these ends, producers and technology use dissimilar cognitive or computational resources, information and problem- solving strategies. (How) can the producer’s intelligence be compared with “smart” production technology?
Each perceives/detects/gathers information, integrates/processes and shares it, but differently. Computational tools perform only specialized tasks utilizing a bounded set of variables. Producers, meanwhile, are adaptable, incorporative and assimilate multifaceted perspectives. How then do their recommendations and decisions/results differ— in nature and to those who rely on them? What purposes do each serve in production?
In production, both producers and technology are called upon for epistemic reasons, tapping alternate viewpoints, and their creative agency. To producers, cognitive work is distributed or delegated in accordance with their specialization. Comparably, musicians and engineers use technology for “offloading mental computation” (Maglio and Wenger, 2000) whereby creators may access altogether “new operations” (Kirsh, 2009, p. 442) for manipulating sound, enhanced control or processing that humans cannot perform unaided. Magnusson proposes that musicians intentionally employ digital instruments as “cognitive extensions”. (Magnusson, 2009, p. 169)
Building on the cognition, decision making, computational co-creation, intelligent mixing and human-computer interface literature, this paper considers the above mentioned comparisons between producer and machine intelligence, between computationally- generated recommendations and real-world decision making. Additionally, it surveys how research currently measures technology against producer performance, and identifies unanswered questions.
