Environmental Noise Classification Using Convolution Neural Networks

被引：2

作者：

Li, Mengyuan ^{[1
]}

Gao, Zhenbin ^{[1
]}

Zang, Xinzhe ^{[1
]}

Wang, Xia ^{[1
]}

机构：

[1] Hebei Univ Technol, Sch Elect & Informat Engn, 5340 Xiping Rd, Tianjin, Peoples R China

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON ELECTRONICS AND ELECTRICAL ENGINEERING TECHNOLOGY (EEET 2018) | 2018年

关键词：

Environmental noise; Convolution Neural Network (CNN); Short-Time Fourier Transform (STFT); Log Mel-Frequency Spectral Coefficients (MFSCs); Tensorflow;

D O I：

10.1145/3277453.3277481

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In order to achieve automatic analysis of environmental noise and related data, a method for classifying noise based on a convolutional neural network (CNN) model was proposed. First, the time-frequency conversion of the noise signal is performed by Short-Time Fourier Transform (STFT), and then the Log Mel-Frequency Spectral Coefficients (MFSCs) of the noise signal are extracted. Finally, the noise-established CNN model is classified. The whole system is implemented on tensorflow. The experimental results show that this method can obtain better classification results, which proves that this method has certain practicability.

引用

页码：182 / 185

页数：4

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