mlsanityData to 14 Oct 2026

Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat Tracking

arXiv 2106.08685 ↗First posted 16 Jun 2021cs.SD cs.LG eess.AScited by 2

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Abstract (arXiv)

This paper presents a novel system architecture that integrates blind source separation with joint beat and downbeat tracking in musical audio signals. The source separation module segregates the percussive and non-percussive components of the input signal, over which beat and downbeat tracking are performed separately and then the results are aggregated with a learnable fusion mechanism. This way, the system can adaptively determine how much the tracking result for an input signal should depend on the input's percussive or non-percussive components. Evaluation on four testing sets that feature different levels of presence of drum sounds shows that the new architecture consistently outperforms the widely-adopted baseline architecture that does not employ source separation.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 8 of its 51 references that are in the map. First posted on arXiv 16 Jun 2021.

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8 of 51 references.

References · 8

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 336
  3. 2016madmom: a new Python Audio and Music Signal Processing Librarycited by 64
  4. 2020Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separationcited by 7
  5. 2016Robust Downbeat Tracking Using an Ensemble of Convolutional Networkscited by 6
  6. 2019Dilated Convolution with Dilated GRU for Music Source Separationcited by 4
  7. 2021Downbeat Tracking with Tempo-Invariant Convolutional Neural Networkscited by 4
  8. 2019Deep Learning-Based Automatic Downbeat Tracking: A Brief Reviewcited by 1

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Citing papers per year: 2021: 1; 2022: 1.

2 papers.