基于U-Net结构的噪声源定位分类方法研究
Research on Noise Source Localization and Classification Method Based on U-Net Structure
黄力1
作者信息
- 1. 福州职业技术学院,福建 福州 350108
- 折叠
摘要
针对噪声污染控制中的噪声源精确定位与分类问题,提出一种基于U-Net结构的方法.该方法通过结合卷积神经网络的编码器-解码器架构与跳跃连接,能够在复杂声场中有效提取多尺度声学特征,实现高精度的噪声源定位和分类.实验结果表明,该方法在不同信噪比(Signal Noise Ratio,SNR)环境下都表现优异,为噪声污染治理提供了有力的技术支撑.
Abstract
Aiming at the problem of accurate location and classification of noise sources in noise pollution control,a method based on U-Net structure is proposed.This method can effectively extract multi-scale acoustic features in complex sound field by combining the encoder-decoder architecture of convolutional neural network with jump connection,and realize high-precision noise source location and classification.The experimental results show that this method performs well in different Signal Noise Ratio(SNR)environments,which provides strong technical support for noise pollution control.
关键词
噪声源定位/U-Net/深度学习/声谱图Key words
noise source localization/U-Net/deep learning/spectrogram引用本文复制引用
出版年
2024
NETL
NSTL
万方数据