Semantic and Semiotic Interplays in Text-to-Audio AI: Exploring Cognitive Dynamics and Musical Interactions

TL;DR

This paper explores how text-to-audio AI systems transform musical creation and perception by analyzing semantic and semiotic interactions, cognitive dynamics, and the potential for new listening modes and cultural insights.

Contribution

It introduces a theoretical framework combining structuralist, semiotic, and cognitive perspectives to understand AI-mediated musical signification and cognition.

Findings

AI models act as quasi-objects of musical signification

They stabilize and destabilize traditional musical forms

They foster new modes of listening and aesthetic reflexivity

Abstract

This paper investigates the emerging text-to-audio paradigm in artificial intelligence (AI), examining its transformative implications for musical creation, interpretation, and cognition. I explore the complex semantic and semiotic interplays that occur when descriptive natural language prompts are translated into nuanced sound objects across the text-to-audio modality. Drawing from structuralist and post-structuralist perspectives, as well as cognitive theories of schema dynamics and metacognition, the paper explores how these AI systems reconfigure musical signification processes and navigate established cognitive frameworks. The research analyzes some of the cognitive dynamics at play in AI-mediated musicking, including processes of schema assimilation and accommodation, metacognitive reflection, and constructive perception. The paper argues that text-to-audio AI models function as quasi-objects of musical signification, simultaneously stabilizing and destabilizing conventional forms while fostering new modes of listening and aesthetic reflexivity.Using Udio as a primary case study, this study explores how these models navigate the liminal spaces between linguistic prompts and sonic outputs. This process not only generates novel musical expressions but also prompts listeners to engage in forms of critical and "structurally-aware listening.", encouraging a deeper understanding of music's structures, semiotic nuances, and the socio-cultural contexts that shape our musical cognition. The paper concludes by reflecting on the potential of text-to-audio AI models to serve as epistemic tools and quasi-objects, facilitating a significant shift in musical interactions and inviting users to develop a more nuanced comprehension of the cognitive and cultural foundations of music.