MétaCan
Menu
Back to cohort
Record W2020594273 · doi:10.1121/1.4786339

Music and science meet at the micro level: Time-frequency methods and granular synthesis

2005· article· en· W2020594273 on OpenAlexaff
Barry Truax

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldComputer Science
TopicMusic Technology and Sound Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer sciencePsychoacousticsAcousticsClass (philosophy)SIGNAL (programming language)QuantumMusical acousticsMusicalPhysicsArtificial intelligenceEpistemologyArtPhilosophyVisual arts

Abstract

fetched live from OpenAlex

Musical research over the last century has become increasingly entwined with the areas of acoustics, psychoacoustics, and electroacoustics. One of the most striking results has been to push the frontiers of models of sound and music to the micro level, what is generally termed microsound. At this level, concepts of frequency and time are conjoined by a quantum relationship, with an uncertainty principle relating them that is precisely analogous to the more famous uncertainty principle of quantum physics. A class of methods of sound synthesis and signal processing known as time-frequency models have their basis at this quantum level such that changes in a signal’s time domain result in spectral alterations and vice versa. One such method, granular synthesis and the granulation of sampled sound, produces results by the generation of high densities of acoustical quanta called grains. Such a radical shift has profound implications for not only our models of sound design, but also for the compositional methods that emerge as well as the role of the composer in guiding complex processes. The paper will argue that these models are examples of a class of complex systems exhibiting emergent form that create a new form of virtual music instrument.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.562
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.021
GPT teacher head0.280
Teacher spread0.259 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations6
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicMusic Technology and Sound StudiesFrench-language works237,207