Paper dossier

Multipath Beat Tracking

Detail viewSimilarity handoff

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

Source and corpus status

Venue

Journal of the Audio Engineering Society

Source slug

jaes

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

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

n/a

This paper did not surface into the current materialized family row set.

Bridge

No materialized row for this family in the resolved run

n/a

This paper did not surface into the current materialized family row set.

Abstract

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.

Authors

  • Bruno Di Giorgi
  • Massimiliano Zanoni
  • Sebastian Böck
  • Augusto Sarti

Neighborhood labels

Topics

2 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

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.

Next handoff

Best next moves from here

01

Check recommendation families

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

Inspect nearby topics

Use Trends to understand whether its attached labels are heating up or cooling down inside the curated corpus.

03

Cross-check evaluation baselines

Use Evaluation to compare the dossier readout against citation and recency baselines for the same resolved family run.