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
3
Authors
2
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
3
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 7
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=0.8318, citation_velocity=0.1800, topic_growth=0.8636, diversity_penalty=0.0000
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1664)
Recent attention: low; used in final ranking (contribution to score: 0.0900)
Topic momentum: high; used in final ranking (contribution to score: 0.2591)
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 6
Multi-topic paper in active topics; no cluster_version on this run so bridge_score was not computed.
Signals: citation_velocity=0.1800, topic_growth=0.8636, diversity_penalty=0.0000
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0630)
Topic momentum: high; used in final ranking (contribution to score: 0.5613)
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 43
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=0.1800, topic_growth=0.8636, diversity_penalty=0.5579
Semantic match: not computed for this run
Recent attention: low; used in final ranking (contribution to score: 0.0540)
Topic momentum: high; used in final ranking (contribution to score: 0.6045)
Cross-cluster signal: not computed for this run
Pool popularity penalty: reduces score when non-zero (contribution to score: -0.1395)
Concert band and wind music are deeply embedded in society and play a significant role in the cultural landscape of many countries, including Germany and Austria, particularly within the amateur music scene. However, this type of music, as well as research on wind and brass instruments in general, remains largely overlooked in the field of music information retrieval (MIR). In this paper, we address this underexplored area by introducing ChoraleBricks, a framework featuring multitrack recordings of ten different chorales, each comprising four musical parts: soprano, alto, tenor, and bass. At its core, ChoraleBricks provides isolated recordings of individual parts performed by a diverse selection of wind instruments, including flute, oboe, clarinet, trumpet, saxophone, baritone horn, trombone, and tuba. These isolated recordings act as building blocks or "bricks" that can be modularly superimposed to create full mixes with varying instrumentation. In addition, ChoraleBricks provides sheet music, time‑aligned symbolic music representations, conducting videos, and reference annotations such as fundamental frequencies and note events. The framework is further enhanced by Python software tools that support parsing, mixing, annotation, and modular combination of the recorded audio material. With all multimedia and software components available as open‑source, ChoraleBricks provides a versatile framework for generating and augmenting datasets for polyphonic wind music. It supports systematic experimentation and facilitates evaluation across various research topics, including multi‑pitch estimation, note transcription, audio alignment, and music education applications.
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 StudiesAnimal Vocal Communication and Behavior
Neighbor surface
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
Wagner Ring Dataset: A Complex Opera Scenario for Music Processing and Computational Musicology
0.587Next 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.