ABSTRACT

This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised machine learning branded as artificial intelligence. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. While LANDR is a product of the so-called ‘big bang’ in machine learning, it could not exist without specific conditions: specific kinds of commensurable data, as well as specific aesthetic and industrial conditions. Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR’s failure at automating other domains of audio engineering. By doing so, we critique the discourse of AI’s inevitability and show the ways in which machine learning must frame or reframe cultural and aesthetic practices in order to automate them, in service of digital distribution, recognition, and recommendation infrastructures.

摘要

本文追溯了自动化音乐母带制作的基础设施政治，以揭示人工智能 (AI) 的当代迭代如何塑造文化生产。这篇论文考察了 LANDR 的出现，这是一个提供自动音乐母带制作的在线平台，建立在被称为人工智能的监督机器学习之上。机器学习将越来越多地成为声音和图像信号处理的一个组成部分，塑造媒体文化的声音、外观和感觉方式。虽然 LANDR 是机器学习中所谓“大爆炸”的产物，但它不可能没有特定条件而存在：特定类型的可公度数据，以及特定的审美和工业条件。反过来，母带作为一种基础设施实践，已成为音乐流通中不可或缺但未被充分研究的部分。在这里，我们分析了机器学习和母带制作的交叉历史，以及 LANDR 在自动化其他音频工程领域的失败。通过这样做，我们批判了人工智能不可避免的话语，并展示了机器学习必须以何种方式构建或重新构建文化和美学实践，以便将它们自动化，为数字分发、识别和推荐基础设施服务。