Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms

• URL: http://arxiv.org/abs/2105.05791v1
• Date: Wed, 12 May 2021 17:04:16 GMT
• Title: Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
• Authors: Ryoto Ishizuka, Ryo Nishikimi, Kazuyoshi Yoshii
• Abstract summary: This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal. To capture the global repetitive structure of drum scores, we introduce a self-attention mechanism with tatum-synchronous positional encoding into the decoder. Experimental results showed that the proposed regularized model outperformed the conventional RNN-based model in terms of the tatum-level error rate and the frame-level F-measure.
• Score: 18.5148472561169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal, in contrast to most conventional ADT methods that estimate the frame-level onset probabilities of drums. To estimate a tatum-level score, we propose a deep transcription model that consists of a frame-level encoder for extracting the latent features from a music signal and a tatum-level decoder for estimating a drum score from the latent features pooled at the tatum level. To capture the global repetitive structure of drum scores, which is difficult to learn with a recurrent neural network (RNN), we introduce a self-attention mechanism with tatum-synchronous positional encoding into the decoder. 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. Experimental results showed that the proposed regularized model outperformed the conventional RNN-based model in terms of the tatum-level error rate and the frame-level F-measure, even when only a limited amount of paired data was available so that the non-regularized model underperformed the RNN-based model.

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