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

@article{Chiu2021DrumAwareEA,
  title={Drum-Aware Ensemble Architecture for Improved Joint Musical Beat and Downbeat Tracking},
  author={Ching-Yu Chiu and A. Su and Yi-Hsuan Yang},
  journal={IEEE Signal Processing Letters},
  year={2021},
  volume={28},
  pages={1100-1104}
}
This letter 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… Expand

Figures and Tables from this paper

MidiBERT-Piano: Large-scale Pre-training for Symbolic Music Understanding
TLDR
An attempt to employ the mask language modeling approach of BERT to pre-train a 12-layer Transformer model for tackling a number of symbolic-domain discriminative music understanding tasks, finding that, given a pretrained Transformer, the models outperform recurrent neural network based baselines with less than 10 epochs of fine-tuning. Expand

References

SHOWING 1-10 OF 51 REFERENCES
Source Separation-based Data Augmentation for Improved Joint Beat and Downbeat Tracking
TLDR
This paper proposes to employ a blind drum separation model to segregate the drum and non-drum sounds from each training audio signal, filtering out training signals that are drumless, and then use the obtained drum andNon-Drum stems to augment the training data. Expand
Musical understanding at the beat level: real-time beat tracking for audio signals
TLDR
A real-time beat tracking system that processes audio signals that contain sounds of various instruments, and correctly tracked beats in 40 out of 42 popular songs in which drums maintain the beat. Expand
A Multi-model Approach to Beat Tracking Considering Heterogeneous Music Styles
TLDR
A new beat tracking algorithm which extends an existing state-of-the-art system with a multi-model approach to represent different music styles and is able to match even human tapping performance. Expand
Joint Beat and Downbeat Tracking with Recurrent Neural Networks
TLDR
A recurrent neural network operating directly on magnitude spectrograms is used to model the metrical structure of the audio signals at multiple levels and provides an output feature that clearly distinguishes between beats and downbeats. Expand
Real-time beat tracking for drumless audio signals: Chord change detection for musical decisions
TLDR
A real-time beat-tracking system that detects a hierarchical beat structure in musical audio signals without drum-sounds and a method of detecting chord changes that does not require chord names to be identified is proposed. Expand
Enhancing downbeat detection when facing different music styles
  • S. Durand, B. David, G. Richard
  • Computer Science
  • 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2014
TLDR
A novel approach for robust downbeat detection using a note accentuation model and a detection of pattern changes are introduced and the time signature is estimated by examining the similarity of frames at the beat level. Expand
An Audio-based Real-time Beat Tracking System for Music With or Without Drum-sounds
TLDR
A real-time beat tracking system that recognizes a hierarchical beat structure comprising the quarter-note, half- note, and measure levels in real-world audio signals sampled from popular-music compact discs is described. Expand
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other
TLDR
A multi-task learning approach for simultaneous tempo estimation and beat tracking of musical audio that is not only able to exploit the mutual information of both tasks by learning a common, shared representation, but can also improve one by learning only from the other. Expand
Downbeat tracking with multiple features and deep neural networks
TLDR
A novel method for the automatic estimation of downbeat positions from music signals that relies on the computation of musically inspired features capturing important aspects of music such as timbre, harmony, rhythmic patterns, or local similarities in both timbre and harmony. Expand
Robust Downbeat Tracking Using an Ensemble of Convolutional Networks
TLDR
A novel state-of-the-art system for automatic downbeat tracking from music signals which takes advantage of the assumed metrical continuity of a song with significant increase in performance compared to the second-best system. Expand
...
1
2
3
4
5
...