audio signal processing; cepstral analysis; neural nets; signal classification; audio clip; audio data; baseline implementation; convolutional layer; convolutional neural network; environmental recording; environmental sound classification; low level representation; max-pooling; mel-frequency cepstral coefficient; public dataset; segmented spectrogram; urban recording; Accuracy; Convolution; Convolutional codes; Neural networks; Pattern recognition; Training; Yttrium; classification; convolutional neural networks; environmental sound;
机译:深度卷积神经网络和环境增强分类的数据增强
机译:基于注意的卷积复发性神经网络,用于环境声分类
机译:使用具有数据增强的正则化深卷积神经网络的环境声音分类
机译:具有多级特征融合的多通道卷积神经网络用于环境声分类
机译:结合卷积神经网络和图形神经网络的图像分类
机译:3D卷积神经网络从佩带的2D卷积神经网络初始化用于工业部件的分类
机译:使用卷积神经网络重新思考环境声音分类:单个特征提取的优化参数调整