Paper year
2025
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
2025
Citations
11
Authors
3
Topic labels
1
Source readout
Journal of the Audio Engineering Society
jaes
Core corpus
Not available yet
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
In top 50 at rank 1
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=0.8442, citation_velocity=0.6600, topic_growth=0.7647, diversity_penalty=0.0000
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1688)
Recent attention: high; used in final ranking (contribution to score: 0.3300)
Topic momentum: high; used in final ranking (contribution to score: 0.2294)
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 14
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.6600, topic_growth=0.7647, diversity_penalty=0.6667
Semantic match: not computed for this run
Recent attention: high; used in final ranking (contribution to score: 0.2310)
Topic momentum: high; used in final ranking (contribution to score: 0.4971)
Cross-cluster signal: not computed for this run
Topic breadth penalty: reduces score when non-zero (contribution to score: -0.1333)
Under-cited
Present in run, outside top 50
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=0.6600, topic_growth=0.7647, diversity_penalty=1.0000
Semantic match: not computed for this run
Recent attention: high; used in final ranking (contribution to score: 0.1980)
Topic momentum: high; used in final ranking (contribution to score: 0.5353)
Cross-cluster signal: not computed for this run
Pool popularity penalty: reduces score when non-zero (contribution to score: -0.2500)
Among all the activities envisioned for the metaverse, music has thus far received comparatively less attention. While virtual concerts and music festivals have been successful in drawing substantial audiences and increasing public attention to the idea of the metaverse, the metaverse is not ready for musicians who decide to take advantage of the distinctive features of socially immersive environments to express themselves and create music together. In this article, the authors analyze the state-of-the-art audio technologies used for the creation of shared, Audio-First immersive environments such as the musical metaverse. This work reveals important issues in consumer electronics that currently prevent the realization of a metaverse compatible with musical activities. These include hardware and software limitations used to create and experience shared immersive environments through real-time audio. This work also emphasizes two key challenges: reducing delays in network and audio processing, and addressing the lack of universal standards for spatial audio systems across different platforms. The authors believe that looking at the metaverse from the point of view of musical technologies will provide practitioners in academia and industry with key insights into what is needed to achieve true real-time activities and support human expression in the metaverse in general.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music Technology and Sound Studies
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
No embedding-backed neighbors available for this paper/version yet.
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.