Multimodel Music Emotion Recognition Using Unsupervised Deep Neural Networks

2019 ◽  
pp. 27-39
Author(s):  
Jianchao Zhou ◽  
Xiaoou Chen ◽  
Deshun Yang
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks

scholarly journals Speech Emotion Recognition Using Deep Neural Networks on Multilingual Databases

2021 ◽  
pp. 21-30
Author(s):  
Syed Asif Ahmad Qadri ◽  
Teddy Surya Gunawan ◽  
Taiba Majid Wani ◽  
Eliathamby Ambikairajah ◽  
Mira Kartiwi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
Speech Emotion Recognition

scholarly journals End-to-End Multimodal Emotion Recognition Using Deep Neural Networks

2017 ◽  
Vol 11 (8) ◽  
pp. 1301-1309 ◽  
Author(s):  
Panagiotis Tzirakis ◽  
George Trigeorgis ◽  
Mihalis A. Nicolaou ◽  
Bjorn W. Schuller ◽  
Stefanos Zafeiriou
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
End To End ◽  
Multimodal Emotion Recognition

scholarly journals Human Emotion Recognition with Electroencephalographic Multidimensional Features by Hybrid Deep Neural Networks

2017 ◽  
Vol 7 (10) ◽  
pp. 1060 ◽  
Author(s):  
Youjun Li ◽  
Jiajin Huang ◽  
Haiyan Zhou ◽  
Ning Zhong
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
Human Emotion

scholarly journals Deep Neural Networks for Emotion Recognition Combining Audio and Transcripts

2018 ◽  
Author(s):  
Jaejin Cho ◽  
Raghavendra Pappagari ◽  
Purva Kulkarni ◽  
Jesús Villalba ◽  
Yishay Carmiel ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks

scholarly journals Singular Learning of Deep Multilayer Perceptrons for EEG-Based Emotion Recognition

2021 ◽  
Vol 3 ◽  
Author(s):  
Weili Guo ◽  
Guangyu Li ◽  
Jianfeng Lu ◽  
Jian Yang
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
High Reliability ◽  
Multilayer Perceptrons ◽  
Learning Technology ◽  
Human Computer Interactions ◽  
Training Process ◽  
Specific Influence

Human emotion recognition is an important issue in human–computer interactions, and electroencephalograph (EEG) has been widely applied to emotion recognition due to its high reliability. In recent years, methods based on deep learning technology have reached the state-of-the-art performance in EEG-based emotion recognition. However, there exist singularities in the parameter space of deep neural networks, which may dramatically slow down the training process. It is very worthy to investigate the specific influence of singularities when applying deep neural networks to EEG-based emotion recognition. In this paper, we mainly focus on this problem, and analyze the singular learning dynamics of deep multilayer perceptrons theoretically and numerically. The results can help us to design better algorithms to overcome the serious influence of singularities in deep neural networks for EEG-based emotion recognition.

LiteEmo: Lightweight Deep Neural Networks for Image Emotion Recognition

2019 ◽  
Author(s):  
Yan-Han Chew ◽  
Lai-Kuan Wong ◽  
John See ◽  
Huai-Qian Khor ◽  
Balasubramanian Abivishaq
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Deep Neural Networks

scholarly journals f-Similarity Preservation Loss for Soft Labels: A Demonstration on Cross-Corpus Speech Emotion Recognition

2019 ◽  
Vol 33 ◽  
pp. 5725-5732
Author(s):  
Biqiao Zhang ◽  
Yuqing Kong ◽  
Georg Essl ◽  
Emily Mower Provost
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Loss Function ◽  
Deep Neural Networks ◽  
Metric Learning ◽  
Loss Functions ◽  
Speech Emotion Recognition ◽  
Subjective Data ◽  
Dual Form ◽  
Deep Metric Learning

In this paper, we propose a Deep Metric Learning (DML) approach that supports soft labels. DML seeks to learn representations that encode the similarity between examples through deep neural networks. DML generally presupposes that data can be divided into discrete classes using hard labels. However, some tasks, such as our exemplary domain of speech emotion recognition (SER), work with inherently subjective data, data for which it may not be possible to identify a single hard label. We propose a family of loss functions, fSimilarity Preservation Loss (f-SPL), based on the dual form of f-divergence for DML with soft labels. We show that the minimizer of f-SPL preserves the pairwise label similarities in the learned feature embeddings. We demonstrate the efficacy of the proposed loss function on the task of cross-corpus SER with soft labels. Our approach, which combines f-SPL and classification loss, significantly outperforms a baseline SER system with the same structure but trained with only classification loss in most experiments. We show that the presented techniques are more robust to over-training and can learn an embedding space in which the similarity between examples is meaningful.

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