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Record W4409305202 · doi:10.24135/ijcmr.v11i1.141

Piano Dreamscapes Ice-Flow Isolation - A case study in binaural mixing for multi-speaker arrays

2025· article· en· W4409305202 on OpenAlexaff
Kasey Pocius

Bibliographic record

VenueInternational Journal of Creative Media Research · 2025
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsConcordia University
Fundersnot available
KeywordsPianoBinaural recordingMixing (physics)Isolation (microbiology)AcousticsFlow (mathematics)Speech recognitionComputer scienceGeologyPhysicsMechanicsBiology

Abstract

fetched live from OpenAlex

Piano Dreamscapes Ice-Flow Isolation is the third in an ongoing series of works focused on transformations of piano improvisations done by myself, as well as collaborators Simon Henley & Neusha Taherian. Composed in remote collaboration throughout late 2020 and early 2021, the piece explores concepts of isolation through spatialization, granular synthesis & spectral transformations. I investigate the process of creating a piece that is both fulfilling to listen to in binaural as well as large speaker array systems from home, with minimal hardware and largely spatialized using the free SPARTA software, without compromising on the compositional quality or techniques used. I also outline some novel expansions on existing spatialization techniques such as timbre spatialization which arose during the composition process.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.351
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.072
GPT teacher head0.407
Teacher spread0.335 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes1
Has abstractyes

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