scholarly journals Research on Music Emotion Recognition Model of Deep Learning Based on Musical Stage Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Cuiqing Huang ◽  
Qiang Zhang
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Technical Problem ◽  
Recognition Rate ◽  
Coincidence Degree ◽  
Recognition Model ◽  
Average Accuracy ◽  
The Times ◽  
Change Of Life ◽  
Different Levels

The change of life style of the times has also prompted the reform of many art forms (including musicals). Nowadays, the audience can not only enjoy the wonderful performances of offline musicals but also feel the charm of musicals online. However, how to bring the emotional integrity of musicals to the audience is a technical problem. In this paper, the deep learning music emotion recognition model based on musical stage effect is studied. Firstly, there is little difference between the emotional results identified by the CRNN model test and the actual feelings of people, and the coincidence degree of emotional responses is as high as 95.68%. Secondly, the final recognition rate of the model is 98.33%, and the final average accuracy rate is as high as 93.22%. Finally, compared with other methods on CASIA emotion set, the CRNN-AttGRU has only 71.77% and 71.60% of WAR and UAR, and only this model has the highest recognition degree. This model also needs to update iteration and use other learning methods to learn at different levels so as to make this model widely used and bring more perfect enjoyment to the audience.

scholarly journals Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

2021 ◽  
Author(s):  
Naveen Kumari ◽  
Rekha Bhatia
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Deep Convolutional Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Learning Models ◽  
Recognition Model ◽  
Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

scholarly journals Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4894 ◽  
Author(s):  
Changzeng Fu ◽  
Chaoran Liu ◽  
Carlos Toshinori Ishi ◽  
Hiroshi Ishiguro
Keyword(s):  
Emotion Recognition ◽  
Research Question ◽  
Weighted Average ◽  
Artificial Agents ◽  
Fusion Method ◽  
Recognition Model ◽  
Decision Level ◽  
Average Accuracy ◽  
Audio Sample ◽  
Level Fusion

Emotion recognition has been gaining attention in recent years due to its applications on artificial agents. To achieve a good performance with this task, much research has been conducted on the multi-modality emotion recognition model for leveraging the different strengths of each modality. However, a research question remains: what exactly is the most appropriate way to fuse the information from different modalities? In this paper, we proposed audio sample augmentation and an emotion-oriented encoder-decoder to improve the performance of emotion recognition and discussed an inter-modality, decision-level fusion method based on a graph attention network (GAT). Compared to the baseline, our model improved the weighted average F1-scores from 64.18 to 68.31% and the weighted average accuracy from 65.25 to 69.88%.

scholarly journals EEG-Based Estimation on the Reduction of Negative Emotions for Illustrated Surgical Images

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7103
Author(s):  
Heekyung Yang ◽  
Jongdae Han ◽  
Kyungha Min
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Emotional Reaction ◽  
Emotional Reactions ◽  
Fatty Tissue ◽  
Eeg Signals ◽  
Recognition Model ◽  
The Difference ◽  
Electroencephalogram Eeg ◽  
Professional People

Electroencephalogram (EEG) biosignals are widely used to measure human emotional reactions. The recent progress of deep learning-based classification models has improved the accuracy of emotion recognition in EEG signals. We apply a deep learning-based emotion recognition model from EEG biosignals to prove that illustrated surgical images reduce the negative emotional reactions that the photographic surgical images generate. The strong negative emotional reactions caused by surgical images, which show the internal structure of the human body (including blood, flesh, muscle, fatty tissue, and bone) act as an obstacle in explaining the images to patients or communicating with the images with non-professional people. We claim that the negative emotional reactions generated by illustrated surgical images are less severe than those caused by raw surgical images. To demonstrate the difference in emotional reaction, we produce several illustrated surgical images from photographs and measure the emotional reactions they engender using EEG biosignals; a deep learning-based emotion recognition model is applied to extract emotional reactions. Through this experiment, we show that the negative emotional reactions associated with photographic surgical images are much higher than those caused by illustrated versions of identical images. We further execute a self-assessed user survey to prove that the emotions recognized from EEG signals effectively represent user-annotated emotions.

scholarly journals Deep Learning-based Categorical and Dimensional Emotion Recognition for Written and Spoken Text

2019 ◽  
Author(s):  
Bagus Tris Atmaja
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Unsupervised Learning ◽  
Recognition Rate ◽  
Product Reviews ◽  
Text Corpus ◽  
Human Emotion ◽  
Written Text ◽  
Spoken Text ◽  
Speech Transcription

The demand for recognizing emotion in text has grown increasingly as human emotion can be expressed via text and manytechnologies, such as product reviews and speech transcription, can benefit from text emotion recognition. The study of text emotionrecognition was established some decades ago using unsupervised learning and a small amount of data. Advancements incomputation hardware and in the development of larger text corpus have enabled us to analyze emotion in the text by moresophisticated techniques. This paper presents a deep learning-based approach for the recognition of categorical and dimensionalemotion from both written and spoken texts. The result shows that the system performs better on both categorical and dimensional task( > 60% accuracy and < 20% error) with a larger dataset compared to a smaller dataset. We also found the recognition rate is affectedby both the size of the data and the number of emotion categories. On the dimensional task, a larger amount of data consistentlyprovided a better result. Recognition of categorical emotion on a spoken text is easier than on a written text, while on dimensional task,the written text yielded better performance.

scholarly journals Deep Learning Based Emotion Recognition and Visualization of Figural Representation

2022 ◽  
Vol 12 ◽  
Author(s):  
Xiaofeng Lu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Environment ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Recognition Rate ◽  
Network Algorithms ◽  
Intelligent Learning Environment ◽  
Graphic Visualization ◽  
Experimental Reference

This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory (CNN-BiLSTM) algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which is at least 3.15% higher than that of other algorithms. Besides, the Recall is at least 7.13% higher than that of other algorithms, and the recognition rate is not less than 90%. Evidently, the improved CNN-BiLSTM algorithm can achieve good recognition results, and provide significant experimental reference for research on learners’ emotion recognition and graphic visualization of expressions in an intelligent learning environment.

GTSception: A Deep Learning EEG Emotion Recognition Model Based on Fusion of Global, Time Domain and Frequency Domain Feature Extraction

2021 ◽  
Author(s):  
Jian Zhao ◽  
ZhiWei Zhang ◽  
Jinping Qiu ◽  
Lijuan Shi ◽  
Zhejun KUANG ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Rapid Development ◽  
Semantic Features ◽  
Hemispheric Differences ◽  
Recognition Model ◽  
Model Based ◽  
Spatial Features ◽  
Convolution Kernels

Abstract With the rapid development of deep learning in recent years, automatic electroencephalography (EEG) emotion recognition has been widely concerned. At present, most deep learning methods do not normalize EEG data properly and do not fully extract the features of time and frequency domain, which will affect the accuracy of EEG emotion recognition. To solve these problems, we propose GTScepeion, a deep learning EEG emotion recognition model. In pre-processing, the EEG time slicing data including channels were pre-processed. In our model, global convolution kernels are used to extract overall semantic features, followed by three kinds of temporal convolution kernels representing different emotional periods, followed by two kinds of spatial convolution kernels highlighting brain hemispheric differences to extract spatial features, and finally emotions are dichotomy classified by the full connected layer. The experiments is based on the DEAP dataset, and our model can effectively normalize the data and fully extract features. For Arousal, ours is 8.76% higher than the current optimal emotion recognition model based on Inception. For Valence, the best accuracy of our model reaches 91.51%.

Pattern recognition and features selection for speech emotion recognition model using deep learning

2020 ◽  
Vol 23 (4) ◽  
pp. 799-806
Author(s):  
Kittisak Jermsittiparsert ◽  
Abdurrahman Abdurrahman ◽  
Parinya Siriattakul ◽  
Ludmila A. Sundeeva ◽  
Wahidah Hashim ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Features Selection ◽  
Recognition Model ◽  
Selection For

scholarly journals Deep Learning Based on CNN for Emotion Recognition Using EEG Signal

2021 ◽  
Vol 17 ◽  
pp. 28-40
Author(s):  
Isah Salim Ahmad ◽  
Shuai Zhang ◽  
Sani Saminu ◽  
Lingyue Wang ◽  
Abd El Kader Isselmou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Negative Emotion ◽  
Recognition System ◽  
Vital Role ◽  
Human Cognition ◽  
Eeg Signals ◽  
Single Model ◽  
Research Attention ◽  
Average Accuracy

Emotion recognition based on brain-computer interface (BCI) has attracted important research attention despite its difficulty. It plays a vital role in human cognition and helps in making the decision. Many researchers use electroencephalograms (EEG) signals to study emotion because of its easy and convenient. Deep learning has been employed for the emotion recognition system. It recognizes emotion into single or multi-models, with visual or music stimuli shown on a screen. In this article, the convolutional neural network (CNN) model is introduced to simultaneously learn the feature and recognize the emotion of positive, neutral, and negative states of pure EEG signals single model based on the SJTU emotion EEG dataset (SEED) with ResNet50 and Adam optimizer. The dataset is shuffle, divided into training and testing, and then fed to the CNN model. The negative emotion has the highest accuracy of 94.86% fellow by neutral emotion with 94.29% and positive emotion with 93.25% respectively. With average accuracy of 94.13%. The results showed excellent classification ability of the model and can improve emotion recognition.

scholarly journals Emotion Recognition from Facial Expression using Deep Learning

2019 ◽  
Vol 8 (6S) ◽  
pp. 91-95
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Image Classification ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Recognition Model ◽  
Emotional Faces

Facial expression recognition is the part of Facial recognition which is gaining more importance and need for it increases tremendously. Though there are methods to identify expressions using machine learning and Artificial Intelligence techniques, this work attempts to use deep learning and image classification method to recognize expressions and classify the expressions according to the images. Various datasets are investigated and explored for training expression recognition model are explained in this paper. Inception Net is used for expression recognition with Kaggle (Facial Expression Recognition Challenge) and Karolinska Directed Emotional Faces datasets. Final accuracy of this expression recognition model using Inception Net v3 Model is 35%(~).

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