Page 48 - 《应用声学》2020年第2期

206 2020 年 3 月

chines and deep neural networks[C]. Proceedings of the quences using a deep neural network[C]. Proceedings of
IEEE International Conference on Machine Learning and the IEEE International Conference on Computer Vision
Applications, IEEE, 2017: 280–285. Workshop, IEEE, 2018: 1460–1469.
[8] Piczak K. Recognizing bird species in audio recordings us- [17] 张建华, 孔繁涛, 吴建寨, 等. 基于改进 VGG 卷积神经网络
ing deep convolutional neural networks[C]. CEUR Work- 的棉花病害识别模型 [J]. 中国农业大学学报, 2018, 23(11):
shop Proceedings, 2012, 43(9): 87–90. 161–171.
[9] Martinsson J. Bird species identification using convolu- Zhang Jianhua, Kong Fantao, Wu Jianzhai, et al. Cotton
tional neural networks[D]. Gothenburg: Chalmers Uni- disease identification model based on improved VGG con-
versity of Technology, 2017. volution neural network[J]. Journal of China Agricultural
[10] 谢将剑, 李文彬, 张军国, 等. 基于 Chirplet 语图特征和深 University, 2018, 23(11): 161–171.
度学习的鸟类物种识别方法 [J]. 北京林业大学学报, 2018,
[18] 刘文定, 李安琪, 张军国, 等. 基于 ROI-CNN 的赛罕乌拉国
40(3): 122–127.
家级自然保护区陆生野生动物自动识别 [J]. 北京林业大学学
Xie Jiangjian, Li Wenbin, Zhang Junguo, et al. Bird
报, 2018, 40(8): 123–131.
species recognition method based on Chirplet spectrogram
Liu Wending, Li Anqi, Zhang Junguo, et al. Automatic
feature and deep learning[J]. Journal of Beijing Forestry
identification method for terrestrial wildlife in Saihanwula
University, 2018, 40(3): 122–127.
National Nature Reserve in Inner Mongolia of northern
[11] 随婷婷, 王晓峰. 一种基于 CLMF 的深度卷积神经网络模
China based on ROI-CNN[J]. Journal of Beijing Forestry
型 [J]. 自动化学报, 2016, 42(6): 875–882.
University, 2018, 40(8): 123–131.
Sui Tingting, Wang Xiaofeng. Convolutional neural net-
[19] Yang T, Long X, Sangaiah A K, et al. Deep detection
works with candidate location and multi-feature fusion[J].
network for real-life traffic sign in vehicular networks[J].
Acta Automatica Sinica, 2016, 42(6): 875–882.
Computer Networks, 2018, 136(8): 95–104.
[12] Goeau H, Glotin H, Vellinga W P, et al. LifeCLEF bird
[20] 付晓峰, 吴俊, 牛力. 小数据样本深度迁移网络自发表情分
identification task 2014[R]. CLEF2014: 585–597.
[13] Wang J C, Wang J F, Weng Y S. Chip design of MFCC 类 [J]. 中国图象图形学报, 2019, 24(5): 753–761.
extraction for speech recognition[J]. Integration-The VLSI Fu Xiaofeng, Wu Jun, Niu Li. Classification of small
Journal, 2002, 32(1/2): 111–131. spontaneous expression database based on deep transfer
[14] Glotin H, Ricard J, Balestriero R. Fast chirplet transform learning network[J]. Journal of Image and Graphics, 2019,
to enhance CNN machine listening-validation on animal 24(5): 753–761.
calls and speech[J]. arXiv: 1611.08749, 2017. [21] 胡满满, 陈旭, 孙毓忠, 等. 基于动态采样和迁移学习的疾病预
[15] Potamitis I, Ntalampiras S, Jahn O, et al. Automatic bird 测模型 [J]. 计算机学报, 2019, 42(10): 2339–2354.
sound detection in long real-field recordings: applications Hu Manman, Chen Xu, Sun Yuzhong, et al. A disease
and tools[J]. Applied Acoustics, 2014, 80(4): 1–9. prediction model based on dynamic sampling and transfer
[16] Marban A, Srinivasan V, Samek W, et al. Estimating learning[J]. Chinese Journal of Computers, 2019, 42(10):
position & velocity in 3D space from monocular video se- 2339–2354.