Navigating Abstract Timbre Spaces: Instrumental Affordances of Concatenative Synthesis in Mosaïque
Bibliographic record
Abstract
This paper explores how accessible tools for corpus-based concatenative synthesis—specifically the Mosaïque software—enable new modes of musical practice by placing artists at the center of real-time sound exploration. Mosaïque democratizes this synthesis method by allowing users—including those with no coding experience—to build and navigate sound corpora in spatialized timbre environments shaped by perceptual descriptors and dimensionality reduction. Emphasizing gesture, improvisation, and listening, the software fosters a practice rooted in embodied interaction and intuitive control. Using a research-creation methodology, we examine how musicians engage with Mosaïque as both instrument and interface. We focus on three key creative areas: corpus creation, timbral navigation and sound design opportunities. Through case studies and first-person accounts, we analyze how artists shape and are shaped by this technology, developing sensorimotor fluency, exploring collective creativity through shared corpora, and challenging conventional compositional paradigms. Situated within electroacoustic theory, human-computer interaction, and creative AI frameworks, we argue that tools like Mosaïque foster inclusive and exploratory musicking practices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".