Testing a New Methodology for Accelerating the Computation of Quadratic Sample Entropy in Emotion Recognition Systems

2018 ◽  
pp. 256-264
Author(s):  
Arturo Martínez-Rodrigo ◽  
Beatriz García-Martínez ◽  
Antonio Fernández-Caballero ◽  
Raúl Alcaraz
Keyword(s):  
Emotion Recognition ◽  
Sample Entropy ◽  
Recognition Systems

scholarly journals An Analysis of the Impact of Spectral Contrast Feature in Speech Emotion Recognition

2021 ◽  
Vol 9 (2) ◽  
pp. 87
Author(s):  
Shreya Kumar ◽  
Swarnalaxmi Thiruvenkadam
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Prediction Accuracy ◽  
Speech Emotion Recognition ◽  
German Language ◽  
Spectral Contrast ◽  
Recognition Systems ◽  
The Impact ◽  
Contrast Feature

Feature extraction is an integral part in speech emotion recognition. Some emotions become indistinguishable from others due to high resemblance in their features, which results in low prediction accuracy. This paper analyses the impact of spectral contrast feature in increasing the accuracy for such emotions. The RAVDESS dataset has been chosen for this study. The SAVEE dataset, CREMA-D dataset and JL corpus dataset were also used to test its performance over different English accents. In addition to that, EmoDB dataset has been used to study its performance in the German language. The use of spectral contrast feature has increased the prediction accuracy in speech emotion recognition systems to a good degree as it performs well in distinguishing emotions with significant differences in arousal levels, and it has been discussed in detail.<div> </div>

scholarly journals Multimodal Affect Models: An Investigation of Relative Salience of Audio and Visual Cues for Emotion Prediction

2021 ◽  
Vol 3 ◽  
Author(s):  
Jingyao Wu ◽  
Ting Dang ◽  
Vidhyasaharan Sethu ◽  
Eliathamby Ambikairajah
Keyword(s):  
Emotion Recognition ◽  
Visual Cues ◽  
Emotion Perception ◽  
Arousal Level ◽  
Multiple Cues ◽  
Model Experiments ◽  
Multimodal Systems ◽  
Recognition Systems ◽  
Change Prediction ◽  
Relative Salience

People perceive emotions via multiple cues, predominantly speech and visual cues, and a number of emotion recognition systems utilize both audio and visual cues. Moreover, the perception of static aspects of emotion (speaker's arousal level is high/low) and the dynamic aspects of emotion (speaker is becoming more aroused) might be perceived via different expressive cues and these two aspects are integrated to provide a unified sense of emotion state. However, existing multimodal systems only focus on single aspect of emotion perception and the contributions of different modalities toward modeling static and dynamic emotion aspects are not well explored. In this paper, we investigate the relative salience of audio and video modalities to emotion state prediction and emotion change prediction using a Multimodal Markovian affect model. Experiments conducted in the RECOLA database showed that audio modality is better at modeling the emotion state of arousal and video for emotion state of valence, whereas audio shows superior advantages over video in modeling emotion changes for both arousal and valence.

scholarly journals Optimizing Residual Networks and VGG for Classification of EEG Signals: Identifying Ideal Channels for Emotion Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Kit Hwa Cheah ◽  
Humaira Nisar ◽  
Vooi Voon Yap ◽  
Chen-Yi Lee ◽  
G. R. Sinha
Keyword(s):  
Image Processing ◽  
Emotion Recognition ◽  
Insular Cortex ◽  
Brain Structures ◽  
Emotion Classification ◽  
Eeg Signals ◽  
Vast Number ◽  
Temporal Lobes ◽  
Recognition Systems

Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processing) is potentially promising in the future as the amount of publicly available EEG databases has been increasing. Architecture of the original ResNet designed for image processing is restructured for optimal performance on EEG signals. The arrangement of convolutional kernel dimension is demonstrated to largely affect the model’s performance on EEG signal processing. The study is conducted on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED), with our proposed ResNet18 architecture achieving 93.42% accuracy on the 3-class emotion classification, compared to the original ResNet18 at 87.06% accuracy. Our proposed ResNet18 architecture has also achieved a model parameter reduction of 52.22% from the original ResNet18. We have also compared the importance of different subsets of EEG channels from a total of 62 channels for emotion recognition. The channels placed near the anterior pole of the temporal lobes appeared to be most emotionally relevant. This agrees with the location of emotion-processing brain structures like the insular cortex and amygdala.

scholarly journals Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare—A Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5015
Author(s):  
Muhammad Anas Hasnul ◽  
Nor Azlina Ab. Ab.Aziz ◽  
Salem Alelyani ◽  
Mohamed Mohana ◽  
Azlan Abd. Abd. Aziz
Keyword(s):  
Artificial Intelligence ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Review Paper ◽  
Recognition System ◽  
Healthcare Systems ◽  
Multimodal Approach ◽  
Health And Wellbeing ◽  
The Subject ◽  
Recognition Systems

Affective computing is a field of study that integrates human affects and emotions with artificial intelligence into systems or devices. A system or device with affective computing is beneficial for the mental health and wellbeing of individuals that are stressed, anguished, or depressed. Emotion recognition systems are an important technology that enables affective computing. Currently, there are a lot of ways to build an emotion recognition system using various techniques and algorithms. This review paper focuses on emotion recognition research that adopted electrocardiograms (ECGs) as a unimodal approach as well as part of a multimodal approach for emotion recognition systems. Critical observations of data collection, pre-processing, feature extraction, feature selection and dimensionality reduction, classification, and validation are conducted. This paper also highlights the architectures with accuracy of above 90%. The available ECG-inclusive affective databases are also reviewed, and a popularity analysis is presented. Additionally, the benefit of emotion recognition systems towards healthcare systems is also reviewed here. Based on the literature reviewed, a thorough discussion on the subject matter and future works is suggested and concluded. The findings presented here are beneficial for prospective researchers to look into the summary of previous works conducted in the field of ECG-based emotion recognition systems, and for identifying gaps in the area, as well as in developing and designing future applications of emotion recognition systems, especially in improving healthcare.

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