MétaCan
Menu
Back to cohort
Record W2263761137

개인화를 위한 음악 추천 시스템 구조

2008· article· ko· W2263761137 on OpenAlexaboutno aff
이상진, 최재훈, 강재우

Bibliographic record

Venue한국멀티미디어학회 학술발표논문집 · 2008
Typearticle
Languageko
FieldComputer Science
TopicInternet of Things and Social Network Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science
DOInot available

Abstract

fetched live from OpenAlex

MP3는 높은 압축률로 인하여 기존에 존재했던 wav형식의 음악파일이 차지했던 영역을 잠식해 들어갔고, 결국 거의 모든 way파일의 영역을 점령 하였지만, 메타파일을 사용하지 않는 음악 검색의 영역이나, 음성의 파형에 기초하여 연구하는 음성 신호 처리학의 영역에서는 여러 가지 문제 때문에 MP3파일을 사용 할 수가 없었다. 따라서 기존의 두 영역의 연구에서는 한정된 저장 공간과 관련된 여러 문제로 인해서, 대량의 데이터를 이용한 연구를 진행하는데 많은 애로사항이 존재했다. 본 논문에서는 캐나다의 McGill 대학에서 개발한 Jaudio를 사용하여 MP3파일로부터 음악파일의 특성을 뽑아내고, 뽑아낸 각 특성들의 성격을 고려하여 음악 파일들을 단계별로 군집화 하는 과정을 트리와 유사하게 구성하고, 이를 통해 개개인의 음악적 기호를 나타내는 방법을 제시 하고자 한다.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0040.013
Scholarly communication0.0100.010
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.004

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 head0.025
GPT teacher head0.258
Teacher spread0.233 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2008
Admission routes1
Has abstractyes

Explore more

Same venue한국멀티미디어학회 학술발표논문집Same topicInternet of Things and Social Network InteractionsFrench-language works237,207