摘要
为解决地铁噪声控制难题,提出一种基于人工智能的地铁噪声控制技术,结合卷积神经网络(Convolutional Neural Network,CNN)与长短期记忆(Long Short Term Memory,LSTM)网络,实现噪声的精确识别、变化趋势预测及实时抑制。实验结果表明,该技术在噪声抑制效果、预测准确性及响应速度方面显著优于传统噪声控制技术,展现了优越的适应性和控制性能。
To solve the problem of subway noise control,an artificial intelligence based subway noise control technology is proposed,which combines Convolutional Neural Network(CNN)and Long Short Term Memory(LSTM)network to achieve accurate identification,trend prediction,and real-time suppression of noise.The experimental results show that this technology is significantly superior to traditional noise control techniques in terms of noise suppression effect,prediction accuracy,and response speed,demonstrating superior adaptability and control performance.
作者
张劲
刘祚连
任权
ZHANG Jin;LIU Zuolian;REN Quan(Qingdao Metro Operation Co.,Ltd.,Qingdao 266000,China)
出处
《电声技术》
2025年第6期31-33,共3页
Audio Engineering
