Halo: Transcription Evolved - Overcoming Issues in Transcribing Modular Video Game Score. / Tatlow, Stephen.

2019.

Research output: Contribution to conferencePaper

Unpublished

Abstract

Halo: Combat Evolved utilises non-linear music to accompany narrative. Music is generated using randomised, algorithmic and interactive, real-time processes. However, transcriptions of the score typically portray a single simplistic variant of this infinitely variable music. As a result, these transcribed portrayals of the score fail to accurately present the working practices and musical design of the composer and sound designer Marty O’Donnell. Relationships between music and interactive content are specifically lost, as is the algorithmic nature of the perceived musical score within the game. To account for these issues, this score can be represented as a series of layers and loops in a modular score similar to that outlined by Medina-Grey. However, even with an understanding of how we could represent the score, the transcription process for Halo: Combat Evolved does not become straightforward. Investigating the “black box” of the video game world through play poses a number of issues, both specifically to Halo and generally to all video games. Amongst the most obvious and frustrating issues: music cannot be fully isolated within the game engine without cracks or hacks, sound layers are mixed ‘automagically’ by the sound engine and gameplay triggers for loops and layers are inconsistent. Elements of randomness combine with contradictory information about the music and sound of the game to cause greater difficulties in identifying loops and layers. These difficulties are not unique to Halo: Combat Evolved. Through an exploration of the process of transcribing the well-recognised music of Halo, this paper demonstrates and suggests applicable methods such as waveform manipulation & analysis, gameplay routing and game engine manipulation that can be applied when examining video game music through “black-box” recordings.
Original languageEnglish
Publication statusUnpublished - Apr 2019

ID: 39728548