Conference Agenda

Focusing on the interplay between fretboard spatiality and right-hand picking mechanics, this paper seeks to develop an expanded transformational model for analyzing rock guitar playing through the lens of early ‘90s guitar instructional videos. Music analysis often prioritizes melodic, harmonic, and structural elements over the physical realities of instrumental performance. The examples taught in these instructional videos highlight the importance of embodied knowledge—how guitarists conceptualize their instrument through physical movement, fingerboard patterns, and picking gestures. By examining techniques such as alternate picking, three-notes-per-string (3NPS) patterns, downward pick slanting (DWPS), and two-way pick slanting, this study explores the systematic and generalized ways rock guitarists navigated fast and complex passages. This paper builds on the work of Jonathan De Souza, Joti Rockwell, and Timothy Koozin on fretboard spatiality, picking transformation and embodied expression.

By treating these instructional videos as vital epistemic tools, this paper argues for a performance-centered analytical framework that prioritizes the embodied and gestural aspects of rock guitar playing. Conventional notation struggles to capture critical elements of technique—such as pick trajectory, string switching efficiency, and micro-adjustments in hand positioning—essential components to any rock guitarist’s technical foundation.

Paul Gilbert’s Intense Rock I (1991), for example, emphasizes the efficiency of 3NPS groupings, demonstrating how even-numbered note patterns align with strict alternate picking to facilitate rapid, fluid motion across the fretboard. Similarly, Yngwie Malmsteen’s 1991 REH video shows how DWPS expands the melodic and harmonic capabilities of the instrument, allowing him to develop licks that push the boundaries of his neoclassical style. Michael Angelo Batio takes this further in Speed Kills (1991) by incorporating two-way pick slanting, enabling him to play hyper-technical licks and scalar runs at machine-like speeds, cheekily proclaiming that he can give you “the keys to the Lamborghini.” While often presented as intuitive or self-evident in the videos, these picking gestures represent an implicit theory of guitar performance that has yet to be fully explored in analytical discourse.

The visual and tactile dimensions of instructional videos provide a unique approach to rock guitar analysis, allowing a deeper understanding of how virtuoso guitarists negotiate their instruments' physical and sonic affordances.

In the mid-1970s, DJ Kool Herc’s extended break beat marked what many hail as hip-hop’s birth; about twenty-five years later, Adam Krims’s 2000 study established an analytical foundation for this beat-based music untethered from traditional Western theory. Another quarter-century later, hip-hop and pop-music scholarship now boasts precise spectral studies of timbre and rich accounts of flow, meter, and sample layering. One crucial domain remains under-examined: the mix. Situated between composition (how sound information is selected and orchestrated) and perception (how listeners interpret what’s heard), mixing shapes recorded tracks through stereo imaging, side-chaining, effects chains, and equalization, transforming what was originally balancing into an intensely creative practice. This study demonstrates that mix analysis can unveil auditory relationships largely inaccessible to conventional methods. Two case studies ground the argument. Ice Cube’s “The N*gga Ya Love to Hate” demonstrates that the Bomb Squad’s densely layered samples and EQ techniques imprint a distinct sonic signature, turning the mix itself into a site of stylistic negotiation between East and West Coast identities. Kendrick Lamar’s “United in Grief” portrays the mix as a narrative engine, crafting contrasts that mirror lyrical meaning. Together, these cases position production mixing as a rich, expressive process that can integrate with existing frameworks and deepen our understanding of recorded musical meaning.

This talk examines why musical media are particularly vulnerable to generative AI while also considering how music’s social and embodied nature insulates it from full automation.

First, I explore the technical challenges AI faces in understanding music. Music Information Retrieval research highlights the difficulty of extracting reliable data from scores and audio, as AI struggles with notation complexities and distinguishing fundamental tones from overtones (Burgoyne et al., 2015; Zbigniew & Wieczorkowska, 2010). Beyond representation issues, AI must process multiple layers of interacting structures—meter, harmony, and phrasing—often leading to incoherent results or direct replication of training data (Margulis, 2014; Rothstein, 1989).

Despite these limitations, AI-generated music does not need to be perfect—only “good enough.” Unlike language or visual media, which can be objectively incorrect, music lacks verifiability. Parallel fifths or unconventional orchestrations are not “wrong” in the same way that factual errors or visual distortions are (Cook, 1998; White, 2022). This flexibility allows AI music to be widely adopted.

However, music’s deep ties to social interaction, embodied performance, and cultural history present barriers to full AI replacement. Hip-hop’s meaning is inseparable from Black American marginalization (Harrison, 2008), and live music is as much about human connection as sound. Music theory’s interdisciplinary reach—spanning analysis, perception, and cultural context—makes it uniquely suited to study AI’s evolving impact on musical creation (Langer, 1942; Palfy, 2022). This talk argues that while AI can replicate structure, it cannot replace music’s human core.