Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification

Kyungdeuk Ko, Sangwook Park, Hanseok Ko

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

3 Citations (Scopus)

Abstract

Pattern classification based on deep network outperforms conventional methods in many tasks. However, if the database for training exhibits internal representation that lacks substantial discernibility for different classes, the network is considered that learning is essentially failed. Such failure is evident when the accuracy drops sharply in the experiments performing classification task where the animal sounds are observed similar. To address and remedy the learning problem, this paper proposes a novel approach composed of a combination of multiple CNNs each separately pre-trained for generating midlevel features according to each class and then merged into a combined CNN unit with SVM for overall classification. For experiment, animal sound database that include 3 classes with 102 species is firstly established. From the experimental results using the database, the proposed method is shown to outperform over prominent conventional methods.

Original languageEnglish
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-379
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
Publication statusPublished - 2018 Oct 26
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: 2018 Jul 182018 Jul 21

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period18/7/1818/7/21

Fingerprint

Support vector machines
Animals
Acoustic waves
Databases
Learning
Pattern recognition
Experiments
Support Vector Machine

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Ko, K., Park, S., & Ko, H. (2018). Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 376-379). [8512408] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512408

Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification. / Ko, Kyungdeuk; Park, Sangwook; Ko, Hanseok.

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. p. 376-379 8512408.

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

Ko, K, Park, S & Ko, H 2018, Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. vol. 2018-July, 8512408, Institute of Electrical and Electronics Engineers Inc., pp. 376-379, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 18/7/18. https://doi.org/10.1109/EMBC.2018.8512408
Ko K, Park S, Ko H. Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July. Institute of Electrical and Electronics Engineers Inc. 2018. p. 376-379. 8512408 https://doi.org/10.1109/EMBC.2018.8512408
Ko, Kyungdeuk ; Park, Sangwook ; Ko, Hanseok. / Convolutional Feature Vectors and Support Vector Machine for Animal Sound Classification. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. pp. 376-379
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