Recognition of Western Black-Crested Gibbon Call Signatures Based on SA_DenseNet-LSTM-Attention Network

Xiaotao Zhou, Ning Wang, Kunrong Hu (), Leiguang Wang, Chunjiang Yu, Zhenhua Guan, Ruiqi Hu, Qiumei Li and Longjia Ye
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Xiaotao Zhou: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Ning Wang: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Kunrong Hu: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Leiguang Wang: Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650024, China
Chunjiang Yu: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Zhenhua Guan: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Ruiqi Hu: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China
Qiumei Li: College of Humanities and Law, Southwest Forestry University, Kunming 650024, China
Longjia Ye: School of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China

Sustainability, 2024, vol. 16, issue 17, 1-24

Abstract: As part of the ecosystem, the western black-crested gibbon ( Nomascus concolor ) is important for ecological sustainability. Calls are an important means of communication for gibbons, so accurately recognizing and categorizing gibbon calls is important for their population monitoring and conservation. Since a large amount of sound data will be generated in the process of acoustic monitoring, it will take a lot of time to recognize the gibbon calls manually, so this paper proposes a western black-crested gibbon call recognition network based on SA_DenseNet-LSTM-Attention. First, to address the lack of datasets, this paper explores 10 different data extension methods to process all the datasets, and then converts all the sound data into Mel spectrograms for model input. After the test, it is concluded that WaveGAN audio data augmentation method obtains the highest accuracy in improving the classification accuracy of all models in the paper. Then, the method of fusion of DenseNet-extracted features and LSTM-extracted temporal features using PCA principal component analysis is proposed to address the problem of the low accuracy of call recognition, and finally, the SA_DenseNet-LSTM-Attention western black-crested gibbon call recognition network proposed in this paper is used for recognition training. In order to verify the effectiveness of the feature fusion method proposed in this paper, we classified 13 different types of sounds and compared several different networks, and finally, the accuracy of the VGG16 model improved by 2.0%, the accuracy of the Xception model improved by 1.8%, the accuracy of the MobileNet model improved by 2.5%, and the accuracy of the DenseNet network model improved by 2.3%. Compared to other classical chirp recognition networks, our proposed network obtained the highest accuracy of 98.2%, and the convergence of our model is better than all the compared models. Our experiments have demonstrated that the deep learning-based call recognition method can provide better technical support for monitoring western black-crested gibbon populations.

Keywords: western black crested gibbon call recognition; data augmentation; bioacoustics; convolutional neural network; attentional mechanism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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