It also considers a number of frameworks including technological determinism and human agency to aid this discussion, aiming to highlight what DSP modelled mastering plugins provide for DAW (Digital Audio Workstation) users universal access to a range of distinctive sonic effects. It discusses the socio-economic factors that lead to the plugins development through the advent of the ‘prosumer’. This chapter discusses the notions of ‘engineering authenticity’ in DSP modeled plugins by discussing Wave’s Abbey Road TG Mastering Chain VST, highlighting how Waves have attempted to translate and emulate the valves, transformers and circuitry of the EMI TG 12410 Transfer Console into cold, hard DSP code. With this mastering, as both a technical process and artist craft, is undergoing a transformative evolution. 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.Īs audio plugin manufacturers model and market hardware devices of the past this, coupled with the advent of Ai (Artificial Intelligence) and Machine Learning mastering platforms, musicians and producers are taking on the mastering process. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR’s failure at automating other domains of audio engineering.
Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. 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. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. 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. This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production.
#CLOUDBOUNCE VS LANDR PROFESSIONAL#
We report the findings from a preliminary evaluation we conducted with three professional mixing engineers and reflect on mixing engineers' comments about the Channel-AI on social media. a corpus of AI features embedded in the Midas HD Console. We demonstrate how these principles have been applied to inform the design of the interaction between expert live audio mixing engineers with the Channel-AI (i.e. We discuss what we believe to be the key design principles and perspectives on the making of intelligent tools for creativity and for experts in the loop. We present the design of the Channel-AI, an embedded AI system which performs instrument recognition and generates parameter settings suggestions for gain levels, gating, compression and equalization which are specific to the input signal and the instrument type.
#CLOUDBOUNCE VS LANDR HOW TO#
In this paper, we contribute to the discussion on how to best design human-centric MIR tools for live audio mixing by bridging the gap between research on complex systems, the psychology of automation and the design of tools that support creativity in music production.
And unlike many other new media firms, LANDR’s connection to its local music scene has been essential to its development, growth, and authority, even as they have since moved on from that scene, and even as the relationship was never fully reciprocal.
While LANDR claims to automate the work of mastering engineers, it appears to expand and morph the definition of mastering itself: it devalues people’s aesthetic labor as it establishes higher standards for recordings online.
To parochialize the machine learning part of what LANDR does, this study spirals in from bigger contexts to smaller ones: LANDR’s place between the new media industry and the mastering industry the music scene in their home city, Montreal, Quebec LANDR use by DIY musicians and independent engineers and, finally, the LANDR interface and the sound it produces in use. Yet we cannot know exactly how much of a role or what role machine learning plays in LANDR. Increasingly, machine learning will become an integral part of the processing of sounds and images, shaping the way our culture sounds, looks, and feels. This article proposes a contextualist approach to machine learning and aesthetics, using LANDR, an online platform that offers automated music mastering and that trumpets its use of supervised machine learning, branded as artificial intelligence (AI).
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