Paper year
2024
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
2024
Citations
2
Authors
3
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
0
Top 50
0
Run label
shadow-generalization-product-candidate-ranking-v1
Snapshot
source-snapshot-shadow-generalization-v1-20260521
Scope: family global | run rank-83787b91ef
Emerging
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Bridge
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Under-cited
No materialized row for this family in the resolved run
This paper did not surface into the current materialized family row set.
Identifying beat positions in music recordings, a central task in Music Information Retrieval (MIR), is commonly referred to as beat tracking. Typically, this involves computing an activation function to reveal frame-wise beat likelihood and then conducting post-processing to derive final beat positions. Existing methods often operate offline, requiring access to the entire music track for processing. In this article, we introduce a real-time beat tracking system based on the predominant local pulse (PLP) concept, originally designed for offline use. Our main contribution is the successful transformation of the PLP-based algorithm into a real-time procedure. Unlike traditional offline methods providing static beat positions, our real-time approach dynamically captures changes in local pulse characteristics with each frame of an audio stream. This yields additional insights, including beat context, beat stability, and beat lookahead for predicting beats in advance. In this way, our system not only demonstrates high controllability for real-time applications but also can operate at zero latency. Additionally, we present experiments comparing our real-time beat tracking system with other models and evaluating the accuracy of our lookahead feature. Finally, we showcase two real-world applications for interactive music making and educational music gaming that creatively leverage our system's output. In summary, our real-time beat tracking system offers a lightweight algorithm that is particularly well-suited for interactive music software development.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Music and Audio ProcessingSpeech and Audio ProcessingMusic 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.
Selective Annotation of Few Data for Beat Tracking of Latin American Music Using Rhythmic Features
0.632Next 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.