Weakly-Supervised Sound Event Detection with Self-Attention

Koichi Miyazaki, Tatsuya Komatsu, Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

In this paper, we propose a novel sound event detection (SED) method that incorporates a self-attention mechanism of the Transformer for a weakly-supervised learning scenario. The proposed method utilizes the Transformer encoder, which consists of multiple self-attention modules, allowing to take both local and global context information of the input feature sequence into account. Furthermore, inspired by the great success of BERT in the natural language processing field, the proposed method introduces a special tag token into the input sequence for weak label prediction, which enables the aggregation of the whole sequence information. To demonstrate the performance of the proposed method, we conduct the experimental evaluation using the DCASE2019 Task4 dataset. The experimental results demonstrate that the proposed method outperforms the DCASE2019 Task4 baseline method, which is based on the convolutional recurrent neural network, and the self-attention mechanism effectively works for SED.

本文言語English
ホスト出版物のタイトル2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ66-70
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5
外部発表はい
イベント2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
継続期間: 2020 5 42020 5 8

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(印刷版)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
国/地域Spain
CityBarcelona
Period20/5/420/5/8

ASJC Scopus subject areas

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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