Volume 38, Number 1, February 2020
|Page(s)||162 - 169|
|Published online||12 May 2020|
Environment Sound Classification System Based on Hybrid Feature and Convolutional Neural Network
National Key Laboratory of Aerospace Flight Dynamics, Xi'an 710072, China
2 School of Astronautics, Northwestern Polytecnical University, Xi'an 710072, China
3 Signals, Images, and Intelligent Systems Laboratory(LISSI/EA 3956), University Paris-Est Creteil, Senart-FB Institute of Technology, 36-37 rue Charpak, 77127 Lieusaint, France
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.
Key words: environment sound / hybrid feature / sound classification / convolutional neural network / filter
关键字 : 环境声音 / 特征融合 / 声音分类 / 卷积神经网络
© 2020 Journal of Northwestern Polytechnical University. All rights reserved.
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