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

Autor:	Chiu, Ching-Yu, Su, Alvin Wen-Yu, Yang, Yi-Hsuan
Rok vydání:	2021
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/LSP.2021.3084504
Popis:	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. Comment: Accepted to IEEE Signal Processing Letters (May 2021)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.08685 Zobrazit plný text záznamu View this record from Arxiv