基于脑功能网络深度学习的车内噪声评价模型  

Research on Vehicle Interior Noise Evaluation Model Based on Stacked Antoencoder and Functional Brain Network

作　　者：邹丽媛 王宏[1] 宋桂秋[1] ZOU Liyuan;WANG Hong;SONG Guiqiu(School of Mechanical Engineering and Automation,Northeastern University,Shenyang 110819,China;Liaoning Water Conservancy Vocational College,Shenyang 110122,China)

机构地区：[1]东北大学机械工程与自动化学院,辽宁沈阳110819 [2]辽宁水利职业学院,辽宁沈阳110122

出　　处：《机械与电子》2020年第5期76-80,共5页Machinery & Electronics

基　　金：辽宁省高等学校创新团队项目(LT2014006);国家重点研发计划(2017YFB1300300)。

摘　　要：研究并构建了一个结合脑电信号处理与深度学习的车内噪声评价模型,该算法通过自我学习实现脑电信号特征提取,使用同步似然方法构建delta、alpha和beta频段的脑功能网络。将3个频带的脑功能网络扁平化处理后作为输入,通过无监督的堆栈自编码器(RSAE)自主提取脑功能网络的特征。通过几个高阶特征训练前后对比,证实了RSAE自主学习到与噪声评价有关的脑神经特征。最终将RSAE与普遍使用的SVM回归模型进行比较,同时将脑功能网络与传统的基于心理声学声音品质的车内噪声评价进行对比。结果表现,所提出的脑功能网络RSAE模型的平均决定系数高达98.69%,明显优于其他方法。In this paper,an in-vehicle noise evaluation model combining EEG signal processing and deep learning was studied and constructed.This algorithm can realize EEG signal feature extraction through self-learning.The synchronous likelihood method was used to construct the brain function network of delta,alpha and beta bands.For the unsteady characteristics of EEG signals,the synchronous likelihood method does better in finding the linear and nonlinear coupling relationship between different channels.The three frequency bands of functional brain networks were flattened as an input.Firstly,the features of the functional brain network were extracted through unsupervised pre-training,and then the initialization weights were used to train the fourlayer network using the invehicle noise scores evaluated by the expert group as the annotation information.Through the comparison of several high-order features before and after training,the validity of RSAE’s selflearned features were confirmed to be useful.In the end,this paper compared the RSAE with SVM regression model,and compared the functional brain network with the traditional psychoacoustic sound quality-based interior noise evaluation.The results show that the proposed vehicle interior noise based on RSAE and functional brain network was better.The average decision coefficient of the functional brain network RSAE model proposed is as high as 98.69%,which is obviously superior to other methods.The results also confirmed the feasibility and effectiveness of the proposed model.

关 键 词：车内噪声评价 堆栈自编码器 脑功能网络分析 同步似然 

分 类 号：R318[医药卫生—生物医学工程]