基于融合特征以及卷积神经网络的环境声音分类系统研究

Translated title of the contribution: Environment Sound Classification System Based on Hybrid Feature and Convolutional Neural Network

Ke Zhang, Yu Su, Jingyu Wang, Sanyu Wang, Yanhua Zhang

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

At present, the environment sound recognition system mainly identifies environment sounds with deep neural networks and a wide variety of auditory features. Therefore, it is necessary to analyze which auditory features are more suitable for deep neural networks based ESCR systems. In this paper, we chose three sound features which based on two widely used filters: the Mel and Gammatone filter banks. Subsequently, the hybrid feature MGCC is presented. Finally, a deep convolutional neural network is proposed to verify which features are more suitable for environment sound classification and recognition tasks. The experimental results show that the signal processing features are better than the spectrogram features in the deep neural network based environmental sound recognition system. Among all the acoustic features, the MGCC feature achieves the best performance than other features. Finally, the MGCC-CNN model proposed in this paper is compared with the state-of-the-art environmental sound classification models on the UrbanSound 8K dataset. The results show that the proposed model has the best classification accuracy.

Translated title of the contributionEnvironment Sound Classification System Based on Hybrid Feature and Convolutional Neural Network
Original languageChinese (Traditional)
Pages (from-to)162-169
Number of pages8
JournalXibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
Volume38
Issue number1
DOIs
StatePublished - 1 Feb 2020

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