Multi-task learning for animal species and group category classification

Donghyeon Kim, Younglo Lee, Hanseok Ko

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

3 Citations (Scopus)


Accurate animal sound classification is an important task in automated animal monitoring system. Such monitoring system is essential for preventing epidemics caused by animal disease. Based on such needs, there has been a variety of efforts to develop an accurate system performing animal sound classification in deep learning framework. Although many research issues and methods to address the issues were introduced, no one has yet to address overcoming the machine learning barriers induced by a single objective function. As learnable parameters only consider a single penalty at the output prediction for training, they cannot capture other characteristics contained in the dataset to extract more generalized prediction. This paper proposes a deep learning based multi-task learning framework for animal sound classification. Both animal species and group classification are performed in an end-to-end learning process. Experimental results show that the proposed multi-task method outperforms single-task method in our recorded animal sound dataset.

Original languageEnglish
Title of host publicationICIT 2019 - Proceedings of the 7th International Conference on Information Technology
Subtitle of host publicationIoT and Smart City
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450376631
Publication statusPublished - 2019 Dec 20
Event7th International Conference on Information Technology: IoT and Smart City, ICIT 2019 - Shanghai, China
Duration: 2019 Dec 202019 Dec 23

Publication series

NameACM International Conference Proceeding Series


Conference7th International Conference on Information Technology: IoT and Smart City, ICIT 2019

Bibliographical note

Funding Information:
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Public Technology Program based on Environmental Policy, funded by Korea Ministry of Environment (MOE) (2017000210001).

Publisher Copyright:
© 2019 Association for Computing Machinery.


  • Animal group
  • Animal species
  • Classification
  • Deep learning
  • Multi-task learning

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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