Unsupervised Feature Learning for EEG-based Emotion Recognition

2017 ◽  
Author(s):  
Zirui Lan ◽  
Olga Sourina ◽  
Lipo Wang ◽  
Reinhold Scherer ◽  
Gernot Muller-Putz
Keyword(s):  
Emotion Recognition ◽  
Feature Learning ◽  
Unsupervised Feature Learning

scholarly journals Unsupervised Feature Learning for Speech Emotion Recognition Based on Autoencoder

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2086
Author(s):  
Yangwei Ying ◽  
Yuanwu Tu ◽  
Hong Zhou
Keyword(s):  
Emotion Recognition ◽  
Data Augmentation ◽  
Feature Learning ◽  
Human Potential ◽  
Speech Emotion Recognition ◽  
Unsupervised Feature Learning ◽  
Learning Techniques ◽  
Speech Data ◽  
Data Division ◽  
Speech Features

Speech signals contain abundant information on personal emotions, which plays an important part in the representation of human potential characteristics and expressions. However, the deficiency of emotion speech data affects the development of speech emotion recognition (SER), which also limits the promotion of recognition accuracy. Currently, the most effective approach is to make use of unsupervised feature learning techniques to extract speech features from available speech data and generate emotion classifiers with these features. In this paper, we proposed to implement autoencoders such as a denoising autoencoder (DAE) and an adversarial autoencoder (AAE) to extract the features from LibriSpeech for model pre-training, and then conducted experiments on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets for classification. Considering the imbalance of data distribution in IEMOCAP, we developed a novel data augmentation approach to optimize the overlap shift between consecutive segments and redesigned the data division. The best classification accuracy reached 78.67% (weighted accuracy, WA) and 76.89% (unweighted accuracy, UA) with AAE. Compared with state-of-the-art results to our knowledge (76.18% of WA and 76.36% of UA with the supervised learning method), we achieved a slight advantage. This suggests that using unsupervised learning benefits the development of SER and provides a new approach to eliminate the problem of data scarcity.

scholarly journals Cystoscopic image classification by unsupervised feature learning and fusion of classifiers

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Seyyed Mohammad Reza Hashemi ◽  
Hamid Hassanpour ◽  
Ehsan Kozegar ◽  
Tao Tan
Keyword(s):  
Image Classification ◽  
Feature Learning ◽  
Unsupervised Feature Learning ◽  
Fusion Of Classifiers

Identification and tracking of vertebrae in ultrasound using deep networks with unsupervised feature learning

2017 ◽  
Author(s):  
Jorden Hetherington ◽  
Mehran Pesteie ◽  
Victoria A. Lessoway ◽  
Purang Abolmaesumi ◽  
Robert N. Rohling
Keyword(s):  
Feature Learning ◽  
Unsupervised Feature Learning ◽  
Deep Networks
Sign in / Sign up

Export Citation Format

Share Document