The efficiency of Convolutional Neural Networks in classifying terse audio snippets of UrbanSounds is evaluated. A deep neural model contains two convolutional layers coupled with Maxpooling plus three fully interconnected (dense) layers. The deep neural model is being trained upon low level description of various urban sound clips with deltas. The efficiency of the neural network is examined on urban recordings and compared with different contemporary approaches. The model obtained 76% validation accuracy that is better than other conventional models which relied only on Mel Frequency Cepstral Coefficients.

中文翻译：

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深度卷积网络用于城市声音分类

评估了卷积神经网络对UrbanSounds的简短音频片段进行分类的效率。一个深度神经模型包含两个与Maxpooling耦合的卷积层以及三个完全互连（密集）层。在对各种带有三角洲的城市声音片段进行低级描述时，将对深度神经模型进行训练。在城市记录中检查了神经网络的效率，并与不同的当代方法进行了比较。该模型获得了76％的验证准确度，优于仅依赖于梅尔频率倒谱系数的其他常规模型。