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Record W2766268321 · doi:10.1525/mp.2017.35.2.144

Blending Between Bassoon and Horn Players

2017· article· en· W2766268321 on OpenAlexaff
Sven-Amin Lembke, Scott Levine, Stephen McAdams

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill University
Fundersnot available
KeywordsUnisonTimbreFormantFrench hornOrchestrationSpeech recognitionPerforming artsAcousticsPsychologyCommunicationComputer scienceMusical

Abstract

fetched live from OpenAlex

Achieving a blended timbre between two instruments is a common aim of orchestration. It relates to the auditory fusion of simultaneous sounds and can be linked to several acoustic factors (e.g., temporal synchrony, harmonicity, spectral relationships). Previous research has left unanswered if and how musicians control these factors during performance to achieve blend. For instance, timbral adjustments could be oriented towards the leading performer. In order to study such adjustments, pairs of one bassoon and one horn player participated in a performance experiment, which involved several musical and acoustical factors. Performances were evaluated through acoustic measures and behavioral ratings, investigating differences across performer roles as leaders or followers, unison or non-unison intervals, and earlier or later segments of performances. In addition, the acoustical influence of performance room and communication impairment were also investigated. Role assignments affected spectral adjustments in that musicians acting as followers adjusted toward a “darker” timbre (i.e., realized by reducing the frequencies of the main formant or spectral centroid). Notably, these adjustments occurred together with slight reductions in sound level, although this was more apparent for horn than bassoon players. Furthermore, coordination seemed more critical in unison performances and also improved over the course of a performance. These findings compare to similar dependencies found concerning how performers coordinate their timing and suggest that performer roles also determine the nature of adjustments necessary to achieve the common aim of a blended timbre.

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 categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.102
GPT teacher head0.376
Teacher spread0.275 · 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 designObservational
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

Citations11
Published2017
Admission routes1
Has abstractyes

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