In: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), pp. Incze Á, Jancsó HB, Szilágyi Z, Farkas A, Sulyok C (2018) Bird sound recognition using a convolutional neural network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. DOI URLĭai YS, Yang J, Dong YW, Zou HP, Hu MZ, Wang B (2021) Blind source separation-based IVA-Xception model for bird sound recognition in complex acoustic environments. DOI URLĭagan U, Izhaki I (2019) Understory vegetation in planted pine forests governs bird community composition and diversity in the eastern Mediterranean region. Table 1 Dataset description of model training dataset used in this study 鸟类中文名称 Chinese nameīuades A, Coll B, Morel JM (2011) Non-local means denoising. The code is open source to Github: CarrieX6/-Xeno-Canto-.git. Its verification accuracy reaches to 96.9%. We used the data of 10 bird sounds from the Xeno-Canto World Wild Bird Sounds public dataset to test the accuracy of bird chirp recognition.Ĭonclusion In this paper, a neural network structure containing self-attention mechanism and center loss function is proposed for bird song recognition. The self-attentive module partially improved the feature representation of key channels the central loss function was used to solve the problem of incompact intra-class features. The fusion features were obtained by splicing the original signal parameters with the modified log-Meier spectral difference parameters the deep learning method was based on the DenseNet121 network structure and incorporated the self-attention module and the central loss function for bird sound recognition. Method: In this paper, we used a fusion feature method combined with deep learning to extract bird sound features. However, low recognition rate is caused to the problems of insufficient feature extraction in traditional bird sound recognition methods. Background: In the ecosystem, birds are an important component, which is crucial for regulating the ecological environment and monitoring biodiversity, and can even assist in predicting natural disasters such as earthquakes and tsunamis by monitoring the movement of birds and listening to their abnormal calls, so bird sound recognition and abnormal call detection have become popular research directions.
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