Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/63662
Title: Explainable automated anuran sound classification using improved one-dimensional local binary pattern and Tunable Q Wavelet Transform techniques
Contributor(s): Akbal, Erhan (author); Barua, Probal Datta  (author); Dogan, Sengul (author); Tuncer, Turker (author); Acharya, U Rajendra (author)
Publication Date: 2023
Early Online Version: 2023
DOI: 10.1016/j.eswa.2023.120089
Handle Link: https://hdl.handle.net/1959.11/63662
Abstract: 

Classification of animal species using animal sounds is a critical issue for bioacoustics work. Especially the determination of anurans (frogs or toads) species can be used as an indicator of climate change. However, counting and classifying anurans in their natural habitat is challenging. Therefore, computer-assisted intelligent systems must be used to determine anuran types correctly. This work collected a new anuran sound dataset and proposed a hand-modeled sound classification system. The collected dataset contains 1536 anuran sounds belonging to 26 anuran species. Furthermore, an improved one-dimensional local binary pattern (1D-LBP) and Tunable Q Wavelet Transform (TQWT) based feature extraction method has been proposed to generate features at both frequency and space domains. Our proposed hand-modeled anuran sound classification architecture comprises of feature extractor (TQWT + improved 1D-LBP), iterative neighborhood component analysis (INCA) selector and k nearest neighbor (kNN) classifier. Our proposed 1D-LBP and TQWT-based anuran sound classification model has obtained a classification accuracy of 99.35% in classifying 26 anuran species. Moreover, we discussed explainable results. In the future, we plan to validate this work by increasing more species in each group.

Publication Type: Journal Article
Source of Publication: Expert Systems with Applications, v.225, p. 1-15
Publisher: Elsevier Ltd
Place of Publication: United Kingdom
ISSN: 1873-6793
0957-4174
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Socio-Economic Objective (SEO) 2020: tbd
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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