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Exploratory interpretations of power law relationships in Debussy’s Syrinx

2022· preprint· en· W4213013041 on OpenAlexaff
Douglas Walter Scott, Zani Ludick

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsComputer scienceVariation (astronomy)Artificial intelligence

Abstract

fetched live from OpenAlex

Background in music analysis. Music analysis often relies on pre-formed concepts such as “binary form”, key centers, or rhythmic tropes to provide templates for comparison between works. To the extent that such tools are based primarily on semantic level considerations, they can present obstacles to translation between different 4E domains of embodied, embedded, extended, and enactive cognition. Power law analysis, representing a context neutral information theoretic approach which is amenable to task specific weighting, can help bridge the gap. Background in applied mathematics. In this paper we investigate how deviations from the near universal phenomenon of logarithmic perception correspond to psychologically tractable interpretations of a musical score. We show that variations in the slope of a straight-line log-log regression and its residuals, together with variation in structural features produced by varying counting methods and levels of analysis correspond to academic and expert assessments in various ways. We then show how wavelet analysis can be used to smooth over level of analysis problems. Aims . We seek to explore how violations of simple power law descriptions (Zipf’s law) relate to structural features of Claude Debussy’s Syrinx for solo flute as in the form of a variety of analyses and performances. Main contribution . Power laws are an important implicit feature of musical notation through such characteristics such as octave equivalence and beat hierarchies, but are rarely explicitly employed as higher-level analytical tools directly, despite the fact that the scale-invariance property and empirical observation suggests that they can be useful at that level too. The simple historical reason is that generating such descriptions, while conceptually straightforward, is quite computationally intensive. Now that computation is readily available it has become possible to extend power law descriptions into these higher levels of analysis with relative ease. Our contribution is to explore some general techniques that can be expanded upon and refined as a broader research program with substantial interdisciplinary implications for the study of perception and cognition generally, since these phenomena obey similar laws. Implications . The present study is correlational in nature, so the direct implications are limited for either music analysis, which provides the interpretive data, or power law representations, which follows well-established practices. However, having established that a set of relatively approachable mathematical operations can readily account for key music-theoretical structural features, the door is opened to a systematic application of the principles involved. This provides an opportunity to state music theories in ways that are precise, falsifiable and directly relatable to modes of description employed in other sciences. Furthermore, because these procedures have broad relevance to many domains outside of music theory, it allows for insights gained from studying musical structure to be applied directly to other, ostensibly unrelated problems.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.052
GPT teacher head0.277
Teacher spread0.225 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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