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Record W2487150041 · doi:10.1525/mp.2016.33.3.287

Auditory Scene Analysis and the Perception of Sound Mass in Ligeti’s Continuum

2016· article· en· W2487150041 on OpenAlexaff
Chelsea Douglas, Jason Noble, Stephen McAdams

Bibliographic record

VenueMusic Perception An Interdisciplinary Journal · 2016
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPerceptionTimbrePsychologySound (geography)Auditory scene analysisRhythmCognitive psychologyCommunicationAcousticsSpeech recognitionComputer scienceMusicalArtPhysicsVisual arts

Abstract

fetched live from OpenAlex

Sound mass has been an influential trend in music since the 1950’s and yet many questions about its perception remain unanswered. Approaching sound mass from the perspective of auditory scene analysis, we define it as a type of auditory grouping that retains an impression of multiplicity even as it is perceived as a perceptual unit. Sound mass requires all markers of the individual identities of sounds to be deemphasized to prevent them from splitting off into separate streams. Seeking to determine how consistent listeners are in their perception of sound mass, and whether it is possible to determine sound parameters and threshold values that predict sound mass perception, we conducted two perceptual studies on Ligeti’s Continuum. This piece consists of an extremely rapid, steady stream of eighth-note dyads with no tempo changes. We addressed the claim by Ligeti and others that the fusion into a continuous texture or sound mass occurs at ca. 20 attacks/s, hypothesizing that other factors such as pitch organization, emergent rhythm, timbre, and register would affect this value. A variety of factors were found to affect sound mass perception, suggesting that the threshold value is not absolute but varies according to principles of auditory scene analysis.

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 score0.740
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.017
GPT teacher head0.285
Teacher spread0.268 · 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

Citations11
Published2016
Admission routes1
Has abstractyes

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