N-DenseNet的城市声音事件分类模型

Urban sound event classification with the N-order dense convolutional network

针对城市声音事件分类领域中现有模型分类准确率不高、泛化能力不强的问题,提出了一种N阶密集卷积神经网络的城市声音事件分类模型。首先,介绍了密集卷积神经网络的结构;其次,基于N阶马尔可夫模型将密集连接改进为N阶有关连接;然后,结合两者提出了一种更适合音频分类的模型--N阶密集卷积神经网络。该模型在避免梯度消失的前提下,有针对性、规律性减少了特征图层之间的连接,更高效地融合了前N特征图层的信息,使得模型的收敛速度更快;最后,为了验证该模型,采用N阶密集卷积神经网络的一阶、二阶子模型,基于UrbanSound8K和Dcase2016数据集开展了城市声音事件分类研究。研究结果表明,其模型准确率分别为83.63%、81.03%,验证了该模型具有良好的分类准确率和泛化能力。

An urban sound event classification model based on the N-order Dense Convolutional Network(abbreviated to N-DenseNet)is proposed for the problems of insufficient classification accuracy and generalization ability of existing models.First,the network structure of the DenseNet is briefly introduced.Then,dense connection in the DenseNet is improved by N-order state-dependent connection based on the Norder Markov model.Furthermore,combining advantages of both the DenseNet and N-order Markov,a novel network architecture,i.e.,the N-DenseNet,is proposed in this paper.Theoretically,the NDenseNet satisfying the premise of alleviating vanishing-gradient,can not only produce efficient integration of feature information from the layers,but also accelerate the convergence speed.Finally,in order to validate advantages of the new model,1-DenseNet and 2-DenseNet are respectively exploited in the urban sound event classification based on the UrbanSound8 Kand Dcase2016 dataset.Experimental results show that the accuracy of the two above-mentioned models is respectively 83.63%and 81.03%,which also demonstrates a higher classification accuracy and a better generalization performance of the N-DenseNet.