Immersive spatialized live music composition with performers: a case study, Le vent qui hurle
Bibliographic record
Abstract
This article presents reflections on theoretical and practical aspects of the composition of immersive spatialized live music with performers through a case study, Le vent qui hurle. This is a piece written for the Ensemble d’oscillateurs founded by Nicolas Bernier at the Faculty of Music of the University of Montreal, semi-modular analog synthesizers, metal sheets and sound spatialization. Initially, I present the research context, discussing the concepts of spatialization and immersion within the frame of live music. I then examine how compositional intentions can be related to the composition of the sound space. I later discuss the relationship between sound and space by introducing spatial attributes and spatialization strategies. Then I illustrate the context in which the piece was created, covering the rehearsal period and the writing of the score. I then present the immersive, spatial and musical composition strategies I used in writing the piece, illustrating techniques that explore the relationship between analog synthesis and the composition of sound space and the relationship between the spatialization created by speakers with the spatialization created with acoustic sources. Finally, I outline future developments. The perspective of this article is mainly oriented toward the compositional dimension and not the technological dimension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".