Tran(ce)sients for large chamber orchestra and audio track with accompanying document
Bibliographic record
Abstract
The purpose of Tran(ce)sients is to find ways to engage an audience in concentrated listening during a concert hall performance of challenging chamber music. For some time now, a point of great interest to me has been what could or will hold one’s attention during a performance, particularly one of challenging music? To answer this question, a search into my own past experiences yielded results which proved to be helpful. This led to a synthesis of influences from three musical styles (namely onkyô, spectral music, and musique concrète instrumentale) and their philosophies, and past compositional processes that have shaped my current aesthetic. In Tran(ce)sients, a 25:13 long work, I attempted to produce concentrated listening by way of analysis of electronically manipulated field recordings and their orchestration for a large chamber orchestra augmented by a small rock ensemble and pre-recorded audio track. The recordings used for Tran(ce)sients represent an abstracted “soundwalk” of my journey from my former apartment in Edmonton to the University of Alberta campus. Using Max software and a USB MIDI controller, the recordings were first manipulated in an improvised manner, and subsequently used in two ways: (1) to supply a set, intermittent recorded background that is an essential part of the score; and (2) to be transcribed and orchestrated for large, acoustic chamber ensemble. The title Tran(ce)sients represents a combination of two words that I feel best explains what this piece is about: a focus on noises we tune out on a day-to-day basis (unwanted, much like transients in the world of studio recording) and the trance -like state that can be experienced while listening to extreme music and the kind of “state of other consciousness” one can experience listening to it. The accompanying document is a paper that will take the form of a brief overview of (1) onkyô, (2) spectral music and (3) instrumental musique concrète followed by a detailed analysis of the entire process behind the piece, starting with manipulating the sounds in Max, moving on to their transcription into notated music and their orchestration for large chamber ensemble, and ending with a summary of the process and the future of the piece. https://doi.org/10.7939/R3PK07J1G Supplementary materials can be found at: https://doi.org/10.7939/R3PK07J1G Audio available for streaming on Aviary: https://ualberta.aviaryplatform.com/collections/1787/collection_resources/136646
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.269 | 0.057 |
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".