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 Four-classes Human Emotion Recognition Via Entropy Characteristic and Random Forest。

2020 ◽  
Vol 49 (3) ◽  
pp. 285-298
Author(s):  
Jian Zhang ◽  
Yihou Min
Keyword(s):  
Random Forest ◽  
Emotion Recognition ◽  
Classification Accuracy ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Eeg Signal ◽  
Differential Entropy ◽  
Eeg Signals ◽  
Human Emotion ◽  
Before And After

Human Emotion Recognition is of vital importance to realize human-computer interaction (HCI), while multichannel electroencephalogram (EEG) signals gradually replace other physiological signals and become the main basis of emotional recognition research with the development of brain-computer interface (BCI). However, the accuracy of emotional classification based on EEG signals under video stimulation is not stable, which may be related to the characteristics of  EEG signals before receiving stimulation. In this study, we extract the change of Differential Entropy (DE) before and after stimulation based on wavelet packet transform (WPT) to identify individual emotional state. Using the EEG emotion database DEAP, we divide the experimental EEG data in the database equally into 15 sets and extract their differential entropy on the basis of WPT. Then we calculate value of DE change of each separated EEG signal set. Finally, we divide the emotion into four categories in the two-dimensional valence-arousal emotional space by combining it with the integrated algorithm, Random Forest (RF). The simulation results show that the WPT-RF model established by this method greatly improves the recognition rate of EEG signal, with an average classification accuracy of 87.3%. In addition, we use WPT-RF model to train individual subjects, and the classification accuracy reached 97.7%.

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.

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 Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins
Keyword(s):  
Deep Learning ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Defect Detection ◽  
Recognition Rate ◽  
Weighted Average ◽  
Wind Turbine Blade ◽  
Evaluation Measures ◽  
Augmentation Techniques ◽  
The Mean

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Speech Emotion Recognition on Indonesian YouTube Web Series Using Deep Learning Approach

2020 ◽  
Author(s):  
Hanina Nuralifa Zahra ◽  
Muhammad Okky Ibrohim ◽  
Junaedi Fahmi ◽  
Rike Adelia ◽  
Fandy Akhmad Nur Febryanto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Learning Approach ◽  
Web Series
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