基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究

李幼军; 黄佳进; 王海渊; 钟宁

doi:10.11959/j.issn.1000-436x.2017294

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基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究

学术论文 | 更新时间：2024-06-05

- 基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究
- Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network
- 通信学报 2017年38卷第12期页码：109-120
- 作者机构：
  
  1. 北京工业大学国际WIC研究院，北京 100124
  2. 磁共振成像脑信息学北京市重点实验室，北京 100124
  3. 脑信息智慧服务北京市国际科技合作基地，北京 100124
  4. 北京未来网络科技高精尖创新中心，北京 100124
- 作者简介：
  
  [ "李幼军（1978-），男，河南栾川人，北京工业大学博士生，主要研究方向为生物信号分析、机器学习及情感计算等。" ]
  [ "黄佳进（1977-），男，贵州遵义人，博士，北京工业大学助理研究员，主要研究方向为人工智能、推荐系统等。" ]
  [ "王海渊（1981-），男，山西朔州人，博士，北京工业大学工程师，主要研究方向智能传感器、人工智能、智慧医疗系统的开发等。" ]
  [ "钟宁（1956-），男，北京人，北京工业大学教授、博士生导师，主要研究方向为人工智能、Web智能、脑信息学、知识发现与数据挖掘、粒计算等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61420106005）);国家重点基础研究发展计划基金资助项目(2014CB744600);国家国际科技合作专项基金资助项目(2013DFA32180)
- DOI：10.11959/j.issn.1000-436x.2017294
  中图分类号： TP181，TP183
- 网络出版日期：2017-12，
  
  纸质出版日期：2017-12-25
- 稿件说明：
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李幼军, 黄佳进, 王海渊, 等. 基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究[J]. 通信学报, 2017,38(12):109-120.

You-jun LI, Jia-jin HUANG, Hai-yuan WANG, et al. Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network[J]. Journal on communications, 2017, 38(12): 109-120.
李幼军, 黄佳进, 王海渊, 等. 基于SAE和LSTM RNN的多模态生理信号融合和情感识别研究[J]. 通信学报, 2017,38(12):109-120. DOI： 10.11959/j.issn.1000-436x.2017294.

You-jun LI, Jia-jin HUANG, Hai-yuan WANG, et al. Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network[J]. Journal on communications, 2017, 38(12): 109-120. DOI： 10.11959/j.issn.1000-436x.2017294.

摘要

为了提高情感识别的分类准确率，提出一种将栈式自编码神经网络（SAE）和长短周期记忆单元循环神经网络（LSTM RNN）融合的多模态融合特征情感识别方法。该方法通过SAE对不同模态的生理特征进行信息融合和压缩，随后用LSTM RNN对长时间周期的融合进行情感分类识别。通过将该方法用到开源数据集中进行验证，得到情感分类准确率达到0.792 6。实验结果表明，SAE对多模态生理特征进行了有效融合，LSTM RNN能够有效地对长时间周期中的关键特征进行识别。

Abstract

In order to achieve more accurate emotion recognition accuracy from multi-modal bio-signal features，a novel method to extract and fuse the signal with the stacked auto-encoder and LSTM recurrent neural networks was proposed.The stacked auto-encoder neural network was used to compress and fuse the features.The deep LSTM recurrent neural network was employed to classify the emotion states.The results present that the fused multi-modal features provide more useful information than single-modal features.The deep LSTM recurrent neural network achieves more accurate emotion classification results than other method.The highest accuracy rate is 0.792 6

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references

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