Channel and frequency attention module for diverse animal sound classification

K. O. Kyungdeuk, Jaihyun Park, David K. Han, Hanseok Ko

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In-class species classification based on animal sounds is a highly challenging task even with the latest deep learning technique applied. The difficulty of distinguishing the species is further compounded when the number of species is large within the same class. This paper presents a novel approach for fine categorization of animal species based on their sounds by using pre-trained CNNs and a new self-attention module well-suited for acoustic signals The proposed method is shown effective as it achieves average species accuracy of 98.37% and the minimum species accuracy of 94.38%, the highest among the competing baselines, which include CNN’s without self-attention and CNN’s with CBAM, FAM, and CFAM but without pre-training.

Original languageEnglish
Pages (from-to)2615-2618
Number of pages4
JournalIEICE Transactions on Information and Systems
Issue number12
Publication statusPublished - 2019


  • Acoustic signal
  • Artificial intelligence
  • CNN
  • Deep learning
  • Self-attention

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence


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