Animal sounds classification scheme based on multi-feature network with mixed datasets

Chung Il Kim, Yongjang Cho, Seungwon Jung, Jehyeok Rew, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


In recent years, as the environment has become an important issue in dealing with food, energy, and urban development, diverse environment-related applications such as environmental monitoring and ecosystem management have emerged. In such applications, automatic classification of animals using video or sound is very useful in terms of cost and convenience. So far, many works have been done for animal sounds classification using artificial intelligence techniques such as a convolutional neural network. However, most of them have dealt only with the sound of a specific class of animals such as bird sounds or insect sounds. Due to this, they are not suitable for classifying various types of animal sounds. In this paper, we propose a sound classification scheme based on a multi-feature network for classifying sounds of multiple species of animals. To do that, we first collected multiple animal sound datasets and grouped them into classes. Then, we extracted their audio features by generating mixed records and used those features for training. To evaluate the effectiveness of our scheme, we constructed an animal sound classification model and performed various experiments. We report some of the results.

Original languageEnglish
Pages (from-to)3384-3398
Number of pages15
JournalKSII Transactions on Internet and Information Systems
Issue number8
Publication statusPublished - 2020 Aug 31


  • Animal sound classification
  • Convolutional neural networks
  • Environmental monitoring

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications


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