Overlapped State Hidden Semi-Markov Model for Grouped Multiple Sequences

Hiromi Narimatsu, Hiroyuki Kasai

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Efficient analysis of multiple sequential data is becoming necessary for identifying sequential patterns of multiple objects of interest. This analysis has major practical and technical importance because finding such patterns necessitates extraction and discovery of latent but meaningful groups of sequences from apparently extraneous but mutually interrelated multiple sequences. However, conventional sequential data analysis methods have not specifically examined this particular technical direction. To tackle this challenge, we propose a new model designated as overlapped state hidden semi-Markov model (OS-HSMM). The model represents the lengths of intervals and overlap among multiple events that are semantically interpretable and appearing across multiple sequences. The salient contribution is that OS-HSMM represents the overlap of two states by extending the state duration probability in HSMM to allow a negative value. Consequently, it handles the state interval and the state overlap simultaneously. Results of our evaluations underscore the effectiveness of our model.

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3397-3401
ページ数5
ISBN(電子版)9781509066315
DOI
出版ステータスPublished - 2020 5
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
CountrySpain
CityBarcelona
Period20/5/420/5/8

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

引用スタイル