MétaCan
Menu
Back to cohort
Record W2417466070

jMIR and ACE XML: Tools for Performing and Sharing Research in Automatic Music Classification

2009· article· en· W2417466070 on OpenAlexaff
Cory McKay, John Burgoyne, Ichiro Fujinaga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsMcGill University
Fundersnot available
KeywordsXMLComputer scienceWorld Wide WebXML frameworkInformation retrievalSuiteXML databasePresentation (obstetrics)Efficient XML InterchangeMedicineGeography
DOInot available

Abstract

fetched live from OpenAlex

This presentation will begin by introducing the research fields of music information retrieval and automatic music classification. The core of the presentation will then be divided into two parts, the first dealing with the jMIR software suite, and the second dealing with the ACE XML file formats. jMIR is a set of free and open source software tools for automatically classifying music in a variety of ways. ACE XML is set of XML-based standardized file formats for storing and sharing the essential information that is related to automatic music classification. Although ACE XML is supported by each of the jMIR components, it is intended for use as a general standard in automatic music classification, and is not limited specifically to jMIR.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.968
Threshold uncertainty score0.503

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 communication0.0010.001
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 head0.259
GPT teacher head0.401
Teacher spread0.142 · 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 designOther design
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

Citations2
Published2009
Admission routes1
Has abstractyes

Explore more

Same topicMusic and Audio ProcessingFrench-language works237,207