Generating Eeg Features From Acoustic Features
28TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2020)(2021)
摘要
In this paper we demonstrate predicting electroencephalography (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN). We predict various types of EEG features from acoustic features. We compare our results with the previously studied problem on speech synthesis using EEG and our results demonstrate that EEG features can be generated from acoustic features with lower root mean square error (RMSE), normalized RMSE values compared to generating acoustic features from EEG features (ie: speech synthesis using EEG) when tested using the same data sets.
更多查看译文
关键词
electroencephalography (EEG), deep learning
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要