arXiv subjects

Tae Gum Hwang

Publications and source records attributed to Tae Gum Hwang.

2 recordsLinked to original sources

arXiv2026

AlignBeat: A Latent Variable Model for Multi-Class Beat Tracking from Partially Labeled Data

Recent neural beat trackers predict beats and downbeats with two independent frame-wise binary classification heads, a convention adopted because a single multi-class head cannot fully exploit datasets that annotate only beats. The heads can disagree, so Beat This moves each predicted downbeat to the nearest predicted beat. We instead predict a sparse set of grid points, each with an event time and one distribution over downbeat, beat and no event, so a downbeat is a beat by construction and no reconciliation is needed. The alignment between grid points and annotated events is latent, and we fit the model by expectation-maximization. Where no downbeat labels were recorded the event class is latent too and is marginalized out. Over eight-fold cross-validation on eighteen datasets, retaining rather than discarding the beat-only labeled data raises beat CMLt by 4.6 points on the datasets that provide it; downbeat CMLt rises 3.6 points over Beat This, with both F-measures moving by less than one point and no post-processing at any stage.

cs.SD↗

arXiv2026

The SMC Blind Spot: A Failure Mode Analysis of State-of-the-Art Beat Tracking

Over the past two decades, the task of musical beat tracking has transitioned from heuristic onset detection algorithms to highly capable deep neural networks (DNN). Although DNN-based beat tracking models achieve near-perfect performance on mainstream, percussive datasets, the SMC dataset has stubbornly yielded low F-measure scores. By testing how well state-of-the-art models detect beats on individual tracks in the SMC dataset, we identify three distinct failure modes: octave errors, continuity errors, and complete tracking failure where all metrics fall below 0.3. We reveal that state-of-the-art models tend to generate "confident-but-wrong" activations. Furthermore, we show that the standard DBN's default minimum tempo of 55 BPM prevents it from inferring the correct tempo for 21\% of SMC tracks, forcing double-tempo predictions on slow music. By exposing such fundamental oversights, we provide concrete directions for improving beat and downbeat detection, specifically emphasizing training data diversification and multi-hypothesis tempo estimation.

eess.AS↗