Birdsong analysis using beta process hidden Markov model

Ryunosuke Hamada, Takatomi Kubo, Kentaro Katahira, Kenta Suzuki, Kazuo Okanoya, Kazushi Ikeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We proposed a new scheme on automatic annotation and analysis for songs of Bengalese finches, that have variability in terms of syllable sequencing. The scheme annotates songs by using the beta process hidden Markov model, a Bayesian non-parametrics method. The annotation was confirmed to agree to the results by the manual annotation by an expert almost perfectly (0.81-1.00) for songs by three out of four Bengalese finches. Our scheme also analyzed the syntactic rules of the birdsongs and found that the difference of the variations in sequencing of four different birds was accounted for by the difference of tutors, giving a new insight to development of syntax.

Original languageEnglish
Title of host publicationIEEE International Workshop on Machine Learning for Signal Processing, MLSP
EditorsTulay Adali, Jan Larsen, Mamadou Mboup, Eric Moreau
PublisherIEEE Computer Society
ISBN (Electronic)9781479936946
DOIs
Publication statusPublished - 2014 Nov 14
Event2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014 - Reims, France
Duration: 2014 Sep 212014 Sep 24

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference2014 24th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2014
CountryFrance
CityReims
Period14/9/2114/9/24

Keywords

  • Automatic annotation
  • Bayesian nonparametrics
  • Beta process hidden Markov model
  • Birdsongs
  • Syntactic rule

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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