[1] MYDLARZ C,SALAMON J,BELLO J P. The implementation of low-cost urban acoustic monitoring devices[J]. Applied Acoustics, 2016,117(Pt 2):207-218. [2] LAFFITTE P,WANG Y,SODOVER D,et al. Assessing the performances of different neural network architectures for the detection of screams and shouts in public transportation[J]. Expert Systems with Applications,2019,117:29-41. [3] BISOT V, SERIZEL R, ESSID S, et al. Acoustic scene classification with matrix factorization for unsupervised feature learning[C]//Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway:IEEE,2016:6445-6449. [4] 李勇, 李应, 余清清. 基于流形学习和SVM的环境声音分类[J]. 计算机工程,2011,37(7):288-290.(LI Y,LI Y,YU Q Q. Environmental sound classification based on manifold learning and SVM[J]. Computer Engineering,2011,37(7):288-290.) [5] 陈波, 俞轶颖. 基于深度神经网络的城市声音分类型研究[J]. 浙江工业大学学报,2019,47(2):199-203.(CHEN B,YU Y Y. Research on urban sound classification model based on deep neural network[J]. Journal of Zhejiang University of Technology,2019,47(2):199-203.) [6] MUN S,SHON S,KIM W,et al. Deep neural network based learning and transferring mid-level audio features for acoustic scene classification[C]//Proceedings of the 2017 IEEE International Conference on the Acoustic, Speech and Signal Processing. Piscataway:IEEE,2017:796-800. [7] SALAMON J,BELLO J P. Deep convolutional neural networks and data augmentation for environmental sound classification[J]. IEEE Signal Processing Letters,2017,24(3):279-283. [8] CHEN Y, GUO Q, LIANG, X, et al. Environmental sound classification with dilate convolutions[J]. Applied Acoustics, 2019,148:123-132. [9] TOKOZUME Y,HARADA T. Learning environmental sounds with end-to-end convolutional neural network[C]//Proceedings of the 2017 IEEE International Conference on the Acoustic,Speech and Signal Processing. Piscataway:IEEE,2017:2721-2725. [10] 冯陈定, 李少波, 姚勇, 等. 基于改进卷积神经网络与动态衰减学习率的环境声音识别算法[J]. 科学技术与工程,2019,19(1):177-182.(FENG C D,LI S B,YAO Y,et al. Environmental sound recognition with improving convolutional neural networks and learning rate decay[J]. Science Technology and Engineering, 2019,19(1):177-182.) [11] PICZAK K J. ESC:dataset for environmental sound classification[C]//Proceedings of the 23rd ACM International Conference on Multimedia. New York:ACM,2015:1015-1018. [12] BODDAPATI V,PETEF A,RASMUSSON J,et al. Classifying environmental sounds using image recognition networks[J]. Procedia Computer Science,2017,112:2048-2056. [13] LI S,YAO Y,HU J,et al. An ensemble stacked convolutional neural network model for environment event sound recognition[J]. Applied Science,2018,8(7):No. 1152. [14] SU Y, ZHANG K, WANG J, et al. Environment sound classification using a two-stream CNN based on decision-level fusion[J]. Sensors,2019,19(7):No. 1733. [15] ABDOLI S, CARDINAL P, KOERICH A L. End-to-end environmental sound classification using a 1D convolutional neural network[J]. Expert Systems with Applications, 2019, 136:252-263. [16] IANDOLA F N,HAN S,MOSKEWICZ M W,et al. SqueezeNet:AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size[EB/OL].[2020-03-05]. https://arxiv.org/pdf/1602.07360.pdf. [17] SALAMON J,JACOBY C,BELLO J P. A dataset and taxonomy for urban sound research[C]//Proceedings of the 22nd ACM International Conference on Multimedia. New York:ACM,2014:1041-1044. [18] 胡航. 现代语音信号处理[M]. 北京:电子工业出版社,2014:72-73.(HU H. Modern Speech Signal Processing[M]. Beijing:Publishing House of Electronics Industry,2014:72-73.) [19] ECKLE K,SCHMIDT-HIEBER J. Acomparison of deep networks with ReLU activation function and linear spline-type methods[J]. Neural Networks,2019,110:232-242. [20] KRIZHEVSKY A,SUTSKEVER I,HINTON G E. ImageNet classification with deep convolutional neural network[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc.,2012:1097-1105. [21] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2020-03-05]. https://arxiv.org/pdf/1409.1556.pdf. [22] 胡挺, 祝永新, 田犁, 等. 面向移动平台的轻量级卷积神经网络架构[J]. 计算机工程,2019,45(1):17-22.(HU T,ZHU Y X, TIAN L, et al. Lightweight convolutional neural network architecture for mobile platforms[J]. Computer Engineering, 2019,45(1):17-22.) [23] KANG Q,ZHAO H,YANG D,et al. Lightweight convolutional neural network for vehicle recognition in thermal infrared images[J]. Infrared Physics and Technology,2019,104:No. 103120. [24] SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout:a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research,2014,15(1):1929-1958. |