Research of a Semantics-Based Music Information Aggregation and Retrieval System for P2P Network
Bibliographic record
Abstract
As the digital music collections in Peer-to-Peer(P2P)network expand larger and larger,music information retrieval(MIR)is becoming a crucial problem in the P2P music-sharing systems.We propose a semantics-based music information aggregation and retrieval system especially for P2P network.First,an extendable music ontology is defined;then we bring the proposal of aggregating the multi-information which contain auto-extracted features,user-annotated descriptors and Web metadata,etc.,and introduce the related extractor-aggregator tool developed based on CLAM Annotator module;furthermore,a method based on the RDFPeers framework is introduced to reposit and query both the static and the dynamic information.The proposed system extends the MIR in P2P network from the traditional title/name keyword retrieval to semantics-based information retrieval.The evaluation of the demonstration model also shows that the proposed multi-information aggregation mentod optimizes the retrieval accuracy and recall.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".