Paper year
2016
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
2016
Citations
9
Authors
4
Topic labels
2
Source readout
Journal of the Audio Engineering Society
jaes
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.
The localization of beat instants is often based on some low-level rhythmic feature extracted from the audio signal (usually an onset detection function), as well as the tempo path that is estimated from it. Going from such descriptors to a sequence of beat instants requires a tracking strategy that models how the beat evolves and offers a good trade-off between stability and responsivity to changes in timing. In the literature this is often performed using dynamic programming methods. In this article we focus on this latter stage of beat tracking and propose a novel strategy based on an efficient generation and joint steering of multiple trackers (paths). This solution is shown to lead to improved computational efficiency with respect to dynamic programming methods, as confirmed by a first set of experiments. In a second set of experiments the proposed method is compared with a broader set of state-of-the-art solutions, though relying on different rhythmic descriptors and beat-tracking strategies, in order to offer a more general assessment of our solution.
Neighborhood labels
Topic labels are imported metadata and can be noisy; use them as coarse navigation hints, not authoritative classifications.
Speech and Audio ProcessingAdvanced Adaptive Filtering Techniques
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
0.631Next 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.