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Channel and Frequency Attention Module for Diverse Animal Sound Classification
Journal article   Open access   Peer reviewed

Channel and Frequency Attention Module for Diverse Animal Sound Classification

Kyungdeuk Ko, Jaihyun Park, David K. Han and Hanseok Ko
IEICE transactions on information and systems, v E102D(12), pp 2615-2618
01 Dec 2019
DOI: https://doi.org/10.1587/transinf.2019EDL8128
url
https://doi.org/10.1587/transinf.2019EDL8128View
Published, Version of Record (VoR) Open

Abstract

Computer Science Computer Science, Information Systems Computer Science, Software Engineering Science & Technology Technology
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.

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8 citations in Web of Science
12 citations in Scopus

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Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Information Systems
Computer Science, Software Engineering
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