Qiumei Li’s research while affiliated with Southwest Forestry University and other places

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Publications (1)

Overall network monitoring routes and network data transmission maps. (a) Environmental factor acquisition sensors; (b) video capture camera; (c) audio capture pickup arrays; (d) wireless network data transmission, primary link: 5.8 GHz wireless network transmission, secondary link: LoRa wireless network transmission; (e) the local authority, where the data are transmitted back to the authority’s servers for storage via the wireless network; (f) the location of the passive acoustic monitoring system in the Ailao Mountains, Chuxiong City, Yunnan Province.
Spectrograms of four different calls of the western black-crested gibbon. (a) simple repetitive syllable calls of the male gibbon; (b) agonistic calls of the female gibbon; (c) weakly modulated syllable calls of the male gibbon; (d) strongly modulated syllable calls of the male gibbon.
Comparison of frequency and amplitude differences in calls of different gibbons.
Examples of spectrograms according to different noise levels of 0 dB, −5 dB, −10 dB and −15 dB.
Diagram of the overall network structure. The overall network structure of this paper (from top to bottom) includes audio data enhancement, DenseNet network and LSTM network based on attention mechanism and feature fusion using PCA.

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Recognition of Western Black-Crested Gibbon Call Signatures Based on SA_DenseNet-LSTM-Attention Network
  • Article
  • Full-text available

August 2024

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26 Reads

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Ning Wang

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Longjia Ye

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.

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