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Record W4406983555 · doi:10.1525/mp.2025.2325705

Instrument Timbre Combinations Influence the Relative Prominence of Perceptual Layers in Orchestral Music

2025· article· en· W4406983555 on OpenAlexaff
Manda Fischer, Stephen McAdams

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Music Perception
Canadian institutionsMcGill UniversityWestern University
Fundersnot available
KeywordsTimbrePerceptionPsychologySpeech recognitionCommunicationArtComputer scienceVisual artsMusicalNeuroscience

Abstract

fetched live from OpenAlex

The stratification of layers of differing prominence (foreground/background) is a common technique in orchestration. Musicians heard 23 excerpts containing foreground and background layers as previously determined by music analysts. A given layer comprised either a single auditory stream of one or more blended instruments or a harmonic or rhythmic background. Two-layer excerpts had either the same, overlapping, or different instrument families (timbre class). First, musicians rated the perceived degree of segregation of musical materials in two-layer and single-stream excerpts. Second, they heard each of the two layers in isolation and then together and rated the relative prominence of the layers. Heterogeneous instrument combinations yielded the greatest difference in relative prominence, followed by overlapping and then homogeneous combinations. Acoustic and score-based descriptors were extracted to quantify their relative contribution to perceptual stratification. Timbre class and between-layer differences in timbre and dynamics played a role, providing evidence of how timbral differences enhance relative prominence in orchestral music. Perceptual segregation was positively but very weakly related to relative prominence, supporting findings that although segregation is necessary to form layers, this mechanism is separable from that which places the streams into the same representational space to allow for the assessment of their relative prominence.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.054
GPT teacher head0.343
Teacher spread0.289 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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