no code implementations • 12 May 2021 • Ryoto Ishizuka, Ryo Nishikimi, Kazuyoshi Yoshii
To mitigate the difficulty of training the self-attention-based model from an insufficient amount of paired data and improve the musical naturalness of the estimated scores, we propose a regularized training method that uses a global structure-aware masked language (score) model with a self-attention mechanism pretrained from an extensive collection of drum scores.
no code implementations • 8 Oct 2020 • Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura, Kazuyoshi Yoshii
This paper describes a neural drum transcription method that detects from music signals the onset times of drums at the $\textit{tatum}$ level, where tatum times are assumed to be estimated in advance.