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Record W6903100234 · doi:10.7939/r3-ps2x-ca22

Tran(ce)sients for large chamber orchestra and audio track with accompanying document

2019· dissertation· en· W6903100234 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2019
Typedissertation
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMIDIActive listeningStudioMusicalFocus (optics)Point (geometry)GuitarSoftware

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.197
Teacher spread0.189 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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