1. Conceptual Framework

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Musique Méta-Concrète is an interdisciplinary research-creation project exploring a new generation of sonic materials: sounds generated by large language models (LLMs) and contemporary AI audio systems. This project extends the historical trajectory of musique concrète—a practice that fractured the bond between sound and its physical cause—into a moment when the “material” of sound increasingly emerges from computational, non-physical, and statistically inferred sources. If Pierre Schaeffer’s sound objects sought to free sound from its origins, musique méta-concrète contends with the inverse problem: sound that originates nowhere recognizable.

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The project proposes a new category of sonic object. AI-generated sounds exist in an ambiguous state: they are constructed from huge archives of recorded and textual data, yet lack any singular physical referent. They resemble field recordings, instrumental gestures, mechanical noise, or environmental atmospheres—but they arrive from latent space, not from microphones. Their sonic “materiality” is virtual yet affectively real. They occupy a liminal territory where familiarity meets uncanniness.

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This project positions these emergent sound objects not as curiosities or substitutes for acoustic reality, but as legitimate artistic materials requiring careful handling, critical thinking, and new compositional approaches. musique méta-concrète takes seriously the idea that LLM-generated audio constitutes its own form of “meta-reality”—a speculative acoustics produced by statistical memory, collective cultural traces, and machine inference.

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By integrating these materials with field recordings, archival fragments, instrumental sources, and live performance, the project creates hybrid sonic ecologies that challenge assumptions about authenticity, authorship, and the nature of listening.

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2. Historical and Artistic Context

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The project arises within a lineage of sonic practices that includes:

• Musique concrète, which reoriented sound around the acousmatic and the recorded.

• Electroacoustic and acousmatic composition, exploring sound as abstract material.

• Post-digital aesthetics, treating artefact, glitch, and mediation as compositional tools.

• Contemporary AI art—a rapidly evolving field often dominated by novelty rather than critical artistic rigor.

 

However, AI-generated audio remains largely unexplored within electroacoustic discourse. Where early computer music pioneers such as Risset, Chowning, and Roads emphasized spectral control and synthesis, LLM-derived sound introduces a different paradigm: one shaped by pattern recognition, probabilistic inference, and the non-transparent internal structures of the model itself.

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This project aims to situate AI in dialogue with these histories instead of outside them. It treats LLM audio not as an “AI effect” but as a new continuity within the evolution of sonic materiality.

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3. Research Questions

The project is driven by a series of interrelated artistic and theoretical questions:

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A. Ontology & Materiality

• What is the status of a sound object with no physical source?

• How does one categorize a sound that mimics the material world but is not derived from it?

 

B. Listening & Perception

• How does the acousmatic experience shift when the listener knows the sound was never “real”?

• What new listening modes arise when origins are not simply hidden but fundamentally absent?

 

C. Authorship & Ethics

• To what extent does the training corpus of an LLM constitute an uncredited archive?

• How do we navigate cultural entanglement when machine-generated audio encodes patterns from diverse, often unacknowledged sources?

 

D. Method & Creation

• What compositional strategies allow AI-generated materials to function not as gimmicks but as integrated, expressive sonic objects?

• How do “meta-concrete” sounds coexist with recordings, live performers, and spatial audio environments?

 

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These questions guide the project’s artistic investigations and frame its contributions to contemporary discourse.

 

4. Artistic Objectives

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1. Develop a coherent compositional methodology for integrating AI-derived sound objects with traditional electroacoustic materials.

2. Create new works (performances, installations, fixed-media pieces) that embody and test this methodology.

3. Generate theoretical writing that articulates the principles of musique méta-concrète as an emerging artistic practice.

4. Engage audiences in new modes of listening that reflect contemporary anxieties and curiosities about AI, authenticity, and the nature of sound.

5. Contribute to ethical AI discourse through an artist-centered, critically reflective perspective.

 

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5. Methodological Approach

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The project is structured around iterative cycles of creation, analysis, and reflection:

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A. Generating “Meta-Concrete” Sound Objects

• Using LLM- and diffusion-based audio models to create novel sound sources.

• Emphasizing non-mimetic generation—timbres, textures, gestures not tied to recognizable objects.

• Documenting failures, anomalies, and emergent behaviours as part of the creative process.

 

B. Comparative Morphological Studies

• Analyzing differences in texture, spectral behaviour, temporal evolution, and perceived agency between:

  • field recordings

  • instrumental sources

  • archival material

  • AI-generated sound

 

C. Hybrid Compositional Practice

• Interweaving AI audio with concrete sounds through:

  • multi-channel spatialization

  • tape montage and diffusion techniques

  • live electronic improvisation

  • performative interventions

 

D. Critical Documentation

• Maintaining detailed process journals.

• Recording structured listening sessions with collaborators and audiences.

• Producing essays, diagrams, and analytic commentary.

 

E. Ethical Reflection

• Collaborating with scholars in digital ethics and sound studies.

• Examining the cultural biases embedded within training data.

• Creating transparency around the provenance of generated materials.

 

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6. Artistic Outputs

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The project will generate multiple interrelated outcomes, including:

• A series of fixed-media compositions exploring different configurations of meta-concrete material.

• Live or hybrid performances, combining electronics, ensemble players, and spatial sound.

• An immersive installation, allowing audiences to inhabit the hybrid sonic ecology directly.

• A set of critical essays, drafting a theoretical foundation for meta-concrete sound.

• Workshops and public presentations, introducing the methods and raising ethical questions for broader audiences.

 

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7. Impact and Significance

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A. Contribution to Artistic Practice

The project introduces a rigorous, historically informed framework for using AI-generated audio in experimental music—moving beyond novelty to deep artistic engagement.

 

B. Contribution to Discourse

It provides language and concepts for understanding AI audio not as imitation but as a new ontological category of sound.

 

C. Contribution to Ethical Understanding

By foregrounding questions of cultural memory and machine-trained archives, it contributes artist-driven perspectives to the rapidly expanding debate around AI and authorship.

 

D. Public Engagement

Audiences encounter a world where acoustic realism is destabilized, prompting reflection on perception, trust, and the shifting boundaries between the real and the synthetic.

 

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8. Conclusion

Musique Méta-Concrète proposes that AI-generated sound represents the next evolution of abstract sonic material—one that is not captured but conjured, not recorded but inferred. By extending the concrete tradition into a speculative, computational realm, the project redefines what a sound object can be, and what forms of listening will matter in an era when sound is increasingly unmoored from its physical origins.

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This project aims not only to create new works but to establish a framework for artists, scholars, and the public to critically engage with a rapidly changing sonic world—one where the material of music is no longer only found in the world, but also in the shifting, latent architectures of the models we build.

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