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Record W2159067642 · doi:10.1109/icme.2006.262674

Interactive Content-Aware Music Browsing using the Radio Drum

2006· article· en· W2159067642 on OpenAlex
Jennifer Murdoch, George Tzanetakis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMusic and Audio Processing
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsMetadataComputer scienceMusic information retrievalDrumDigital audioInterface (matter)Space (punctuation)MultimediaKey (lock)Information retrievalWorld Wide WebAudio signalSpeech recognitionMusical

Abstract

fetched live from OpenAlex

Portable digital music players are becoming pervasive and the size of personal digital music collections has been steadily increasing (5-10 thousand tracks are common today). The emerging area of music information retrieval (MIR) deals with all aspects of managing, analyzing and organizing music in digital formats. The majority of work in MIR follows a search/retrieval paradigm. More recently, the importance of browsing as an interaction paradigm has been realized and several novel interfaces have been proposed. In this paper, we describe a tangible interface for content-aware browsing of music collections. The radio drum is a gestural interface based on capacitance sensors that can detect the x,y,z positions of two drum sticks in a 3D volume. We describe two possible mappings that can be used for browsing music collections without relying on metadata. The first is an explicit mapping of tempo and beat strength, and the second is a music similarity space using audio feature extraction and a self organizing map (SOM)

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.908
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.056
GPT teacher head0.250
Teacher spread0.194 · 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

Quick stats

Citations8
Published2006
Admission routes1
Has abstractyes

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