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Influence of “indeterminate music” on visual art: a phenomenological, semiotic and fractal exploration

2021· article· en· W3160184621 on OpenAlexaff
Pinaki Gayen, Archi Banerjee, Shankha Sanyal, Sayan Nag, Priyadarshi Patnaik, D. Ghosh

Bibliographic record

VenueJournal of Physics Conference Series · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicComplex Systems and Time Series Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndeterminacy (philosophy)PaintingJohn CageMusicalMusic and emotionArtPsychologyVisual artsPhilosophyMusic historyArt historyEpistemologyPerformance art

Abstract

fetched live from OpenAlex

Abstract Indeterminacy in music, a well known neo-avant-garde approach of composing sound where some features of a musical work are left open to chance or to the interpreter’s free choice, became noticeable among some American music composers such as John Cage, Earle Brown, Morton Feldman and Christian Wolff in the mid 20th century. Simultaneously, a group of artists from the West created “abstract expressionism” in visual arts, which showed a strong resemblance with this “indeterminate music”, both using two kinds of abstract languages. The commonality among these two art forms is the free improvisation of creative activity. The correspondence between the indeterminate music induced emotions and the depicted emotional contents in paintings is a relevant area which is still scientifically unexplored. To investigate the same, we conducted a case study where a visual artist listened to four music clips composed by the above mentioned musicians and created four paintings. The visual artist is strongly inspired by the abstract expressionist methods and these methods lend well to inspirational work based on listening to indeterminate music.To understand the nature of intermediality, if any, that exists between “indeterminate music” and “evoked abstraction” in paintings, the artist’s phenomenological interpretations of the process was compared with detailed semiotic analysis of specific musical and visual elements and the nature of their relatedness. Fractal analysis in the form of Detrended Fluctuation Analysis (DFA) was also done on both the acoustic waveforms of the chosen music clips and the corresponding paintings to explore possible correlations. Some unique findings yielded from the analysis, which hint toward strong correlation between the prominent musical features of indeterminate music and the prominent visual features of the paintings inspired by them. This novel study has the potential to offer both new methodology as well as better understanding the features of intermediality between “indeterminate music” and visual art.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.049
GPT teacher head0.239
Teacher spread0.190 · 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 designTheoretical or conceptual
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

Citations3
Published2021
Admission routes1
Has abstractyes

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