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An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Muhammad Adeel Asghar, Muhammad Jamil Khan, Muhammad Rizwan, Raja Majid Mehmood, Sun-Hee Kim
2020 Sensors  
In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods.  ...  Four pre-trained Deep Neural Networks (DNN) are used to extract deep features.  ...  features of multiple neural networks using Differential Entropy based Channel Selection and Deep Feature clustering (DFC).  ... 
doi:10.3390/s20133765 pmid:32635609 fatcat:fdp54265o5gyzaabmv24crqq4a

Table of Contents

2021 2021 Innovations in Intelligent Systems and Applications Conference (ASYU)  
Umoh and Ahmed Abdul Ibrahim, RMWSaug: Robust Multi-window Spectrogram Augmentation Approach for Deep Learning based Speech Emotion Recognition Onder Coban, Ali Inan and Selma Ayse Ozel, Fine-grained Kinship  ...  Period Using Machine Learning Techniques Nearest Neighbor Algorithms on GPUs Based on Deep Convolutional Neural Networks Kirill Sheshulin, Aleksandr Chuvakov and Anton Ivaschenko, Deep Learning Models  ... 
doi:10.1109/asyu52992.2021.9598945 fatcat:x7s5fbqesvfbxcl4f7ytilvnzm

Utterance Level Feature Aggregation with Deep Metric Learning for Speech Emotion Recognition

Bogdan Mocanu, Ruxandra Tapu, Titus Zaharia
2021 Sensors  
In addition, an end-to-end neural embedding approach is introduced, based on an emotionally constrained triplet loss function.  ...  In this paper, we introduce a novel speech emotion recognition method, based on the Squeeze and Excitation ResNet (SE-ResNet) model and fed with spectrogram inputs.  ...  The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  ... 
doi:10.3390/s21124233 fatcat:aaq3ttoexjd2lk2tng3ngd43ia

Guest Editorial: Advanced Machine-Learning Methods for Brain-Machine Interfacing or Brain-Computer Interfacing

Kaijian Xia, Yizhang Jiang, Yudong Zhang, Wen Si
2021 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
used for knowledge transfer is proposed, and its rationality is explained from the perspective of probability density estimation. 3) Clustering techniques are used to select source domains so as to reduce  ...  for epilepsy signals recognition.  ...  a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges.  ... 
doi:10.1109/tcbb.2021.3078145 fatcat:hojaokclpjhgpcqob5jbwrlktm

MIFAD-Net: Multi-Layer Interactive Feature Fusion Network With Angular Distance Loss for Face Emotion Recognition

Weiwei Cai, Ming Gao, Runmin Liu, Jie Mao
2021 Frontiers in Psychology  
Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss.  ...  differences and high inter-class similarity in facial emotion recognition.  ...  Second, the convolutional neural network trains the model by learning the filter's To achieve the best results, multi-scale inference approaches are commonly used in computer vision models.  ... 
doi:10.3389/fpsyg.2021.762795 pmid:34744943 pmcid:PMC8569934 fatcat:cab7sqna3zesppwzfcptijsiz4

Speech Emotion Recognition: A Review Paper

2021 International Journal for Research in Applied Science and Engineering Technology  
Artificial Neural Network (ANN), and K-nearest neighbour (KNN) are some of the techniques used to distinguish various emotions from human expression.  ...  We examined the fundamentals of a speech emotion recognition system and explored various pre-processing, feature extraction, and classification techniques for the system in this paper.  ...  Multi-level multi-head fusion attention, RNN The Multi-Level Multi-Head Fusion Attention mechanism and a recurrent neural network are used in this paper to present a multimodal method for speech emotion  ... 
doi:10.22214/ijraset.2021.33656 fatcat:tznu42bhvrd5tb7wguzqbqm6be

Soft computing and intelligent systems: techniques and applications

Sabu M. Thampi, El-Sayed M. El-Alfy
2019 Journal of Intelligent & Fuzzy Systems  
An optimal model parameter selection algorithm for behavioral modeling of Radio Frequency (RF) Power Amplifiers is presented in [20] .  ...  Deep learning has some successful applications in ecology and ecosystems. In [6] , a plant recognition system is introduced using traditional feature extraction and deep learning methods.  ... 
doi:10.3233/jifs-169905 fatcat:nktxom4rmvce3lulfrxf6gcxie

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Angkoon Phinyomark, Erik Scheme
2018 Big Data and Cognitive Computing  
Finally, directions for future research in EMG pattern recognition are outlined and discussed.  ...  ; and (2) methods based on feature learning with a special emphasis on "deep learning".  ...  Convolutional Neural Network (CNN) The CNN (or ConvNet) may be the most widely used deep learning model in feature learning and is by far the most popular deep learning method for EMG pattern recognition  ... 
doi:10.3390/bdcc2030021 fatcat:h24h4mj6xvgdtgeg5xrmqre6nm

Speech Emotion Recognition with Heterogeneous Feature Unification of Deep Neural Network

Wei Jiang, Zheng Wang, Jesse S. Jin, Xianfeng Han, Chunguang Li
2019 Sensors  
to low emotion recognition performance in this work.  ...  Automatic speech emotion recognition is a challenging task due to the gap between acoustic features and human emotions, which rely strongly on the discriminative acoustic features extracted for a given  ...  [20] proposed a multi-modal emotion recognition by using semi-supervised learning and multiple neural networks.  ... 
doi:10.3390/s19122730 fatcat:n4pgdcbcnzannd5p3ystsyuclm

Table of Contents

2021 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)  
media paper 105 Cansu Özkan and Kaya Oguz, Selecting Emotion Specific Speech Features to Distinguish One Emotion from Others paper 106 Hasan Zan, Abdulnasır Yıldız, Sleep Arousal Detection Using One Dimensional  ...  Traffic Hyperparameters for Long-Term Traffic Forecasting paper 19 Gozde Yolcu Oztel, Vision-based Road Segmentation for Intelligent Vehicles using Deep Convolutional Neural Networks paper 20 Sarah  ... 
doi:10.1109/inista52262.2021.9548429 fatcat:mrjsewx2orggblilwn7qozxn6q

Table of Contents

2018 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)  
184 BEHAVIOR ANALYSIS OF VISUALIZATION IMAGES USING DEEP LEARNING APPROACH 185 Multi-Instance Iris Recognition Using an Augmented Webber Local Descriptor based Feature Vector 186 A Survey Paper  ...  Method for Early Detection of Mild Cognitive Impairment 127 Emotion Recognition Using Multimodal Approach 128 Traffic Light Detection and Recognition for Self Driving Cars using Deep Learning  ... 
doi:10.1109/iccubea.2018.8697655 fatcat:jvjgmcrh3fhxtkf4kyydawnkiq

Speech Sentiment Analysis Using Hierarchical Conformer Networks

Peng Zhao, Fangai Liu, Xuqiang Zhuang
2022 Applied Sciences  
Compared with the emotion expression of most text languages, speech is more intuitive for human emotion, as speech contains more and richer emotion features.  ...  Multimodality has been widely used for sentiment analysis tasks, especially for speech sentiment analysis.  ...  Funding: We are grateful for the support of the National Natural Science Foundation of Shandong ZR202011020044.  ... 
doi:10.3390/app12168076 fatcat:thhbhsfk7ralxfyy72l7eyaokq

Assessing Effectiveness of Exercised Variants of Machine Learning Techniques

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
However, there are different schemes (perception based, instance-based and logic based) to provide an effective classification, prediction, and data recognition in terms of characterizing the features  ...  This investigational study provides a comprehensive study on different algorithmic approaches which are categorized in terms of classification-based, prediction-based and segmentation based approaches.  ...  Face Recognition The face recognition is used in security purpose, finding the human age, emotion recognition, etc.  ... 
doi:10.35940/ijitee.d1781.029420 fatcat:3dig3j6ja5hovmazntsm3ldt3m

2021 Index IEEE Transactions on Multimedia Vol. 23

2021 IEEE transactions on multimedia  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  -that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021.  ...  Li, A New Approach for Character Recognition of Multi-Style Vehicle License Video Sequences in HEVC.  ... 
doi:10.1109/tmm.2022.3141947 fatcat:lil2nf3vd5ehbfgtslulu7y3lq

Multi-Layer Hybrid Fuzzy Classification Based on SVM and Improved PSO for Speech Emotion Recognition

Shihan Huang, Hua Dang, Rongkun Jiang, Yue Hao, Chengbo Xue, Wei Gu
2021 Electronics  
In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation.  ...  In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer.  ...  In addition to analyzing only speech signals, the emotion recognition system based on deep learning also uses the multimodal recognition method to analyze emotions combined with attributes such as text  ... 
doi:10.3390/electronics10232891 fatcat:3hwpfho5zne2xmin7q3vrqhwtq
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