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Record W7044417150

When timbre blends musically: perception and acoustics underlying orchestration and performance

2015· dissertation· en· W7044417150 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsTimbreOrchestrationPerceptionRelevance (law)Active listeningContext (archaeology)MusicalPitch (Music)Identification (biology)
DOInot available

Abstract

fetched live from OpenAlex

Blending or contrasting instrumental timbres are common techniques employed in orchestration.Both bear a direct relevance to the perceptual phenomenon of auditory fusion, which in turn depends on a series of acoustical cues.Whereas some cues relate to musical aspects, such as timing and pitch relationships, instrumentation choices more likely concern the acoustical traits of instrument timbre.Apart from choices made by composers and orchestrators, the success of timbre blending still depends on precise execution by musical performers, which argues for its relevance to musical practice as a whole.This thesis undertakes a comprehensive investigation aiming to situate timbre blend in musical practice, more specifically addressing the perceptual effects and acoustical factors underlying both orchestration and performance practice.Three independent studies investigated the perception of blend as a function of factors related to musical practice, i.e., those derived from musical context and realistic scenarios (e.g., pitch relationships, leadership in performance, room acoustics).The first study establishes generalized spectral descriptions for wind instruments, which allow the identification of prominent features assumed to function as their timbral signatures.Two listening experiments investigate how these features affect blend by varying them in frequency, showing a critical perceptual relevance.The second study considers two other listening experiments, which evaluate perceived blend for instrument combinations in dyads and triads, respectively.Correlational analyses associate the obtained blend measures with a wide set of acoustic measures, showing that blend depends on pitch and temporal relationships as well as the previously identified spectral features.The third study extends the previous ones, addressing factors related to musical performance by investigating the timbral adjustments performers employ in blending with one another, as well as their interactive relationship.Timbral adjustments can be shown to be made towards the musician leading the performance.All studies contribute to a greater understanding of blend as it applies to musical and orchestration practice.Their findings expand previous research and provide possible explanations for discrepancies between hypotheses made in the past.Together, the conclusions drawn allow us to propose a general perceptual theory for timbre blend as it applies to musical practice, which considers the musical material and spectral relationships among instrument timbres as the determining factors. of me and guiding me around potential pitfalls, and IDMIL for the many lusophone friends I have made over the years.Writing this thesis has been rather smooth, and I owe it to the persons introducing me to L A T E X and those improving the experience, namely, Mark Zadel, Finn Upham, and Marcello Giordano.Across the road at the Centre for Interdisciplinary Research in Music Media and Technology (CIRMMT), I would first like to thank Scott Levine, formerly with the Sound Recording Area, whose technical contribution to Experiment 5 has been invaluable to its success, which reached a scope of complexity impossible to realize alone and in such a short time.I would also like to thank Martha de Francisco for her co-supervision of Scott's

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.036
GPT teacher head0.262
Teacher spread0.227 · 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.

Study designTheoretical or conceptual
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

Citations1
Published2015
Admission routes1
Has abstractyes

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