Announcement Capture System in Real Environments Using Recurrent Neural Network

Shintaro Nakazawa, Takeshi Sasaki

研究成果: Conference contribution

抄録

Announcement is useful transmission mean. It is used in various places. Important information such as evacuation guidance in the case of emergency is often transmitted in the announcements. But some people miss it due to various factors. In this paper, we propose an announcement capture system to resolve that problems. The system consists of three steps: Firstly, announcements are detected in real environmental recordings. Secondly, duration of the announcement is extracted from detected sounds. Finally, extracted announcements are output in some form on user's device. For the first step to develop the proposed system, we validated the performance of the classifier to detect announcements. The classifier was trained in some features using BLSTM which is one of the methods of machine learning. In the validation experiment, the performances of each classifiers trained by varying features were compared. As results of the validation, the feature in the human perceptual aspect was effective to identify announcements. In addition to the result, it was considered that there is a possibility to improve the performance of announcement detection using the feature in the acoustic aspect. However, to incorporate the acoustic feature, reviewing the hyperparameters and removing surrounding sounds of the announcement are required.

本文言語English
ホスト出版物のタイトルProceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1046-1051
ページ数6
ISBN(電子版)9781728166674
DOI
出版ステータスPublished - 2020 1
イベント2020 IEEE/SICE International Symposium on System Integration, SII 2020 - Honolulu, United States
継続期間: 2020 1 122020 1 15

出版物シリーズ

名前Proceedings of the 2020 IEEE/SICE International Symposium on System Integration, SII 2020

Conference

Conference2020 IEEE/SICE International Symposium on System Integration, SII 2020
国/地域United States
CityHonolulu
Period20/1/1220/1/15

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 生体医工学
  • 制御およびシステム工学
  • 安全性、リスク、信頼性、品質管理
  • 制御と最適化
  • 器械工学

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