Paper year
2026
Detect emerging, bridge-candidate, and undercited papers inside a curated audio-ML corpus, then expose the signals behind every recommendation.
Paper dossier
Review source metadata, abstract, authors, topics, and local similarity context before moving into explanation and ranking views.
Paper year
2026
Citations
0
Authors
7
Topic labels
3
Source readout
Transactions of the International Society for Music Information Retrieval
tismir
Core corpus
6
Ranking readout
This block uses the same resolved ranking run as Recommended. Ranks here are materialized paper_scores ranks; live Emerging may be reordered by the bounded ML scorer. Family rank is global within each family, but rank is only shown when this paper lands inside the surfaced top 50.
Families present
3
Top 50
2
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
Present in run, outside top 50
Emerging: embedding slice fit vs included-corpus centroid (title+abstract), plus citation velocity and topic growth; not universal relevance. Bridge signal not used here.
Signals: semantic=0.8114, citation_velocity=0.0000, topic_growth=0.8266, diversity_penalty=0.0000
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1623)
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentum: high; used in final ranking (contribution to score: 0.2480)
Cross-cluster signal: not computed for this run
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
In top 50 at rank 35
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.0000, topic_growth=0.8266, diversity_penalty=0.0000
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentum: high; used in final ranking (contribution to score: 0.5373)
Cross-cluster signal: not computed for this run
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
In top 50 at rank 21
Low-cite candidate pool (see docs/candidate-pool-low-cite.md v0): core corpus, recency floor, citation ceiling, title+abstract gate; popularity penalty among pool members only. Semantic and bridge not yet modeled.
Signals: citation_velocity=0.0000, topic_growth=0.8266, diversity_penalty=0.0000
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentum: high; used in final ranking (contribution to score: 0.5786)
Cross-cluster signal: not computed for this run
Pool popularity penalty: reduces score when non-zero (contribution to score: 0.0000)
The Real World Computing (RWC) Music Database has been a cornerstone of Music Information Retrieval (MIR) research for over two decades, offering high‑quality recordings across multiple genres, including popular, classical, and jazz music. Beyond its extensive audio collection, the dataset is enriched by aligned Musical Instrument Digital Interface (MIDI) encodings and complementary annotations, including beat, structure, and chord labels, making it a valuable resource for music structure analysis, beat tracking, chord recognition, automatic transcription, and music synchronization. Originally, the RWC audio material was distributed on physical media and made available for purchase at a nominal price. A significant development, announced and initiated with this paper, is the release of the RWC dataset under a Creative Commons license, making it freely accessible for research purposes. This transition significantly enhances the dataset's usability and supports broader adoption within the MIR research community. We outline the steps taken to enable this release and share a vision for transforming RWC into a community‑driven resource that promotes open research and collaboration. With the audio recordings now hosted on Zenodo, we also discuss strategies for dataset maintenance, annotation expansion, and reproducibility through collaborative platforms such as GitHub. This shift promotes transparency and inclusivity, helping to ensure the dataset's continued relevance for cutting‑edge MIR research. We further revisit the historical significance of the RWC dataset, incorporating insights from an interview with its original creator, Masataka Goto, and provide an overview of its current applications and future potential. In summary, by embracing an open and community‑supported approach, we aim not only to renew the dataset's impact and preserve its legacy within the MIR community but also to shed light on broader best practices for open, collaborative, and sustainable research infrastructures.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingMusic Technology and Sound StudiesDiverse Musicological Studies
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
Next handoff
01
Use Recommended to see whether this paper behaves like an emerging or undercited signal in the current ranked feed, or how it appears on the bridge preview / diagnostics view.
02
Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.
03
Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.