Extending the acoustic ensemble through spectral and temporal transformations in real-time.
Bibliographic record
Abstract
The paradigm of live performance mixing acoustics and electronics has predominantly focused on simple background “tape music,” human players performing highly structured sample-based music (e.g., using the ABLETON LIVE software), or reactive systems that respond to player qualities such as timing, pitch, and so on. In this talk I will present my approach to improvised “laptop performance” that focuses on the transformation of acoustic players in real-time. Rather than simply altering the acoustic content in the manner of an effect processor, the goal is to capture notes and phrases in short-term memory and to re-articulate the material so that it presents a new gestural inflection and timbral content that can be completely novel or suggestive of other players’ sound. The system presented utilizes a hybrid system combining spectral analysis and feature extraction with block-based temporal processing and a feedback delay network. The interaction paradigm of “scrubbing” the intermediate time/frequency representation is used to generate the ultimate output. The result in an ensemble context is an extended palette that can “keep up” with the musical dialog while eliciting the subtle textural qualities of acoustic players. [This work was supported by NSF Grant 0757454 and CIRMMT/McGill University Fellowships.]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".