SMT-V: The Society for Music Theory Videocast Journal
SMT-V is the open-access, peer-reviewed video journal of the Society for Music Theory. Founded in 2014, SMT-V publishes video essays that showcase research in music theory in a dynamic, audiovisual format, presented so as to have the potential to engage both specialists within the field as well as interested viewers outside the music theory community. The journal features a supportive and collaborative production process, and publishes several videos each year. Read more about SMT-V here.
Latest Issue: 12.2 (March 2026)
“Optimize This! Why Do We Care if an AI Can Write Songs?”
Andrew Goldman (Indiana University)
AI systems like Suno take in text prompts, and output audio of original songs that are compellingly human-sounding. Here, I describe three ways to compare human and AI generated music. To frame my discussion, I juxtapose my original song (“Optimize!”) with a song that Suno generated on the same topic. We can compare products, focusing on the musical work (as audio, or symbolic notation), and consider whether AI-generated music passes the Turing test, or whether there are features that sound artificial. I analyze my own song, and Suno’s. We can also compare processes. How did I produce a work of music compared to how Suno did it? Again, I compare my process with Suno’s. Both product- and process-based comparisons aim to explain features of musical scores and audio, but music is more than what is encoded in such representations. Thus, a third kind of comparison resists this work-based ontology of music: a comparison of musical practice. My song is more than the notes on the page; there was a social motivation to write about optimization. In contrast, AI music’s sociality is homuncular: it only has social purpose because the humans who use the technology do.
Keywords: Artificial intelligence; songwriting; music as practice; music cognition; creativity