Abstract: A noise robust urban sound event classification model based on 2-order dense convolutional network using dual features(D-2-DenseNet) is proposed,which aims at the problems of insufficient robustness of current models. Firstly,the brief introduction of the method of noise adding and robust processing is presented. Moreover,a dual feature mutual compensation algorithm and 2-order dense convolutional network is presented. Meanwhile,a noise robust urban sound event classification model based on 2-DenseNet using dual features,i.e.D-2-DenseNet is proposed. Theoretically,D-2-DenseNet combines the advantages of feature compensation and 2-order dense convolutional neural network. The dual feature mutual compensation adaptive algorithm can effectively extract audio information and reduce noise interference to improve noise robustness. Finally,in order to validate advantages of the D-2-DenseNet,this new model is exploited in the urban sound event classification based on Dcase2016 datasets. Under conditions of channel noise and environmental noise,the experiment shows that the accuracy of the network is respectively 77.12% and 75.52%,which has added 8.51% and 10.38% compared with baseline. The noise robustness of D-2-DenseNet are also effectively verified.

Key words: sound event classification, noise robust, dual features mutual compensation, 2-order dense convolutional network, 2-order dense convolutional network using dual features