Paper year
2025
Paper dossier
Cross‑Modal Approaches to Beat Tracking: A Case Study on Chopin Mazurkas
Detail viewSimilarity handoff
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Citations
0
Authors
4
Topic labels
3
Source readout
Source and corpus status
Venue
Transactions of the International Society for Music Information Retrieval
Source slug
tismir
Corpus placement
Core corpus
Similarity rows
6
Ranking readout
Where this paper lands in the current run
Run shadow-generalization-product-candidate-ranking-v1Top 50 surfaced
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 38
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=0.8484, citation_velocity=0.0000, topic_growth=0.8636, diversity_penalty=0.0000
Why this surfaced | 3 used | 1 penalty | 1 not computed
Embedding slice fit (corpus centroid)used
Embedding slice fit (corpus centroid): high; used in final ranking (contribution to score: 0.1697)
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.2591)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Similarity penaltypenalty
Similarity penalty: reduces score when non-zero (contribution to score: 0.0000)
Bridge
In top 50 at rank 22
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=0.8636, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.5613)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Topic breadth penaltypenalty
Topic breadth penalty: reduces score when non-zero (contribution to score: 0.0000)
Under-cited
In top 50 at rank 14
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=0.8636, diversity_penalty=0.0000
Why this surfaced | 2 used | 1 penalty | 2 not computed
Semantic matchnot computed
Semantic match: not computed for this run
Recent attentionused
Recent attention: low; used in final ranking (contribution to score: 0.0000)
Topic momentumused
Topic momentum: high; used in final ranking (contribution to score: 0.6045)
Cross-cluster signalnot computed
Cross-cluster signal: not computed for this run
Pool popularity penaltypenalty
Pool popularity penalty: reduces score when non-zero (contribution to score: 0.0000)
Abstract
Within music information retrieval (MIR) research, numerous beat‑tracking systems have been developed, targeting either audio recordings or symbolic representations such as MIDI files. However, the differences between these approaches, their respective strengths and weaknesses, and the potential for combining them have received limited attention. In this article, we compare two conceptually different beat trackers: an audio‑based model that operates frame by frame and a symbolic‑based model using an event‑driven approach. Specifically, we analyze the performance of two pretrained systems: the audio beat tracker madmom and the symbolic beat tracker Performance MIDI‑to‑Score (PM2S). Our evaluation is based on a cross‑modal dataset of Chopin's Mazurkas (Maz‑5), which includes multiple audio recordings and MIDI representations automatically transcribed from audio. As a key contribution, we standardize the post‑processing pipelines for the frame‑based and event‑based beat trackers to ensure comparability and explore various late‑fusion methods within a unifying framework. Our results highlight the effectiveness of these fusion strategies in leveraging the strengths of both modalities while providing valuable insights into the performance of existing beat‑tracking models.
Authors
Neighborhood labels
Topics
3 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 StudiesMusicology and Musical Analysis
Neighbor surface
Similar papers
6 total neighborsEmbedding v1-title-abstract-1536-cleantext-r3
Similar papers use a separately configured neighbor embedding; it may differ from the embedding version used by the current ranked run.
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