On April 1, Megan Lavengood (George Mason University) joined the Department of Music’s Musicology Colloquium for a lecture entitled “The Common Cold: Using Data Science to Define the Winter Topic in Video Game Music.” The CDH co-sponsored the event.
Take a moment and remember the last movie you watched. You probably remember the general arc of the story, how much (or little) you enjoyed it, and maybe even your favorite scene or moment. But if you think carefully, can you remember the music? How the soundtrack interacted with the visual scene, emphasizing moments of tension, heightening emotional reactions, and cuing you in to additional, extramusical information? Such questions relating the ability of music to speak to the human experience, situated within the ongoing development of our technologically mediated world, speak to core tenets of humanistic scholarship. This humanistic approach to research often investigates music’s multifaceted functionality in multimedia by drawing on a diverse set of digital, analytical, and interdisciplinary methodologies to incorporate new perspectives relevant to larger threads of humanistic discourse.
Professor Megan L. Lavengood’s colloquium lecture “The Common Cold: Using Data Science to Define the Winter Topic in Video Game Music” unpacked how specific combinations of musical characteristics can be combined to cue associations of winter within video game music. By using musical analysis to precisely tag specific musical features for over 150 examples, Professor Lavengood created a curated dataset that encoded musical features such as tonality, instrumentation, and meter. After such detailed and careful data collection, Professor Lavengood then worked with collaborator Evan Williams to explore this dataset by creating visualizations of common trends in musical features across the different examples. By exploring the dataset in this way, Professor Lavengood identified specific musical characteristics that were prevalent across many examples, portraying how these different features might come together to aurally signify winter within this genre.
Drawing in undergraduate students, graduate students, faculty, and public attendees, this accessible event unpacked each step in the research process, detailing the steps of example selection, musical feature data tagging and encoding, and even final analyses. This lecture encouraged every participant to reflect on how music can be “wintery” and the ways in which different game producers and consumers reinscribe such connotations with each repetition and playthrough. Through a music theoretical approach and perspective, this lecture foregrounded how digital means of analysis can be leveraged in service of answering research questions central to humanistic discourse.
Abstract: This presentation draws on my collaborative work with Evan Williams, a music-theorist-turned-data-scientist. It models a new approach to theorizing topics via music informatics. Whereas other topic theory research typically relies on the author to recognize connections among musical features (Agawu 1991, Monelle 2006, Atkinson 2019), we allow the data to suggest its own groupings, revealing relationships that may not otherwise be apparent. Our case study is the winter topic. We chose to focus specifically on winter in video game music, as video game music leaves little ambiguity around what the music ought to signify. Video games commonly have an icy or snowy area, complete with cold-weather creatures, landscapes, game mechanics, and music for the player to encounter. Our dataset has over 160 examples of such music, representing games on all mainstream platforms (Nintendo, PlayStation, computer, etc.) and spanning the years 1987–2020. Each example is tagged with its musical features: instrumentation, meter, tonality, presence/absence of arpeggiated accompaniment, amount of reverb, and drum pattern. We use Python, the PyData stack, and standard data science algorithms like PageRank alongside traditional music analytical techniques to illuminate several facets of the winter topic. Through this case study of winter video game music, we present a model of analysis that could easily be adapted to suit any repertoire or topic.
Speaker Biography: Megan Lavengood is Assistant Professor and Director of Music Theory at George Mason University. Her research primarily deals with popular music, timbre, synthesizers, and recording techniques. Her current research project focuses on topic theory and video game music. Before COVID, she was a soprano in a Renaissance quartet.
Natalie Miller is a third-year PhD Candidate in the Department of Music, serving on the 2021-2022 Musicology Colloquium Committee. Her research investigates how music influences user immersion in multimedia with a particular focus on audiovisual interactions.
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