HUSCAP logo Hokkaido Univ. logo

Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >

Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting

Files in This Item:
JASA-Vol144No5-p2709.pdf1.78 MBPDFView/Open
Please use this identifier to cite or link to this item:http://hdl.handle.net/2115/76057

Title: Automatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting
Authors: Harakawa, Ryosuke Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Akamatsu, Tomonari Browse this author
Issue Date: 8-Nov-2018
Publisher: Acoustical Society of America
Journal Title: The Journal of the Acoustical Society of America
Volume: 144
Issue: 5
Start Page: 2709
End Page: 2718
Publisher DOI: 10.1121/1.5067373
Abstract: This paper presents a method for automatic detection of fish sounds in an underwater environment. There exist two difficulties: (i) features and classifiers that provide good detection results differ depending on the underwater environment and (ii) there are cases where a large amount of training data that is necessary for supervised machine learning cannot be prepared. A method presented in this paper (the proposed hybrid method) overcomes these difficulties as follows. First, novel logistic regression (NLR) is derived via adaptive feature weighting by focusing on the accuracy of classification results by multiple classifiers, support vector machine (SVM), and k-nearest neighbors (kNN). Although there are cases where SVM or k-NN cannot work well due to divergence of useful features, NLR can produce complementary results. Second, the proposed hybrid method performs multi-stage classification with consideration of the accuracy of SVM, k-NN, and NLR. The multistage acquisition of reliable results works adaptively according to the underwater environment to reduce performance degradation due to diversity of useful classifiers even if abundant training data cannot be prepared. Experiments on underwater recordings including sounds of Sciaenidae such as silver croakers (Pennahia argentata) and blue drums (Nibea mitsukurii) show the effectiveness of the proposed hybrid method.
Rights: Copyright 2018 Acoustical Society of America. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the Acoustical Society of America. The following article appeared in R. Harakawa, T. Ogawa, T. Akamatsu, M. Haseyama, "Automatic Detection of Fish Sounds Based on Multi-stage Classification Including Logistic Regression via Adaptive Feature Weighting," The Journal of the Acoustical Society of America, vol. 144, no. 5, pp. 2709-2718, 2018. and may be found at https://doi.org/10.1121/1.5067373.
Type: article
URI: http://hdl.handle.net/2115/76057
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 原川 良介

Export metadata:

OAI-PMH ( junii2 , jpcoar )

MathJax is now OFF:


 

Feedback - Hokkaido University