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20132023
most citedEnd-to-End Multimodal Emotion Recognition using Deep Neural Networks

755 citations · 2.7k across the 91 of their papers we have counts for

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Showing 2017Show all

7 papers · 1 filter

cs.SD2017★ 19 cited

auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks

Michael Freitag, Shahin Amiriparian, Sergey Pugachevskiy +2

auDeep is a Python toolkit for deep unsupervised representation learning from acoustic data. It is based on a recurrent sequence to sequence autoencoder approach which can learn re…

cs.SD2017★ 13 cited

Learning audio sequence representations for acoustic event classification

Zixing Zhang, Ding Liu, Jing Han +2

Acoustic Event Classification (AEC) has become a significant task for machines to perceive the surrounding auditory scene. However, extracting effective representations that captur…

cs.SD2017★ 62 cited

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Zixing Zhang, Jürgen Geiger, Jouni Pohjalainen +3

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge.…

stat.ML2017★ 3 cited

Fast Single-Class Classification and the Principle of Logit Separation

Gil Keren, Sivan Sabato, Björn Schuller

We consider neural network training, in applications in which there are many possible classes, but at test-time, the task is a binary classification task of determining whether the…

cs.CV2017★ 755 cited

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

Panagiotis Tzirakis, George Trigeorgis, Mihalis A. Nicolaou +2

Automatic affect recognition is a challenging task due to the various modalities emotions can be expressed with. Applications can be found in many domains including multimedia retr…

cs.CV2017

Deep Structured Learning for Facial Action Unit Intensity Estimation

Robert Walecki, Ognjen, Rudovic +3

We consider the task of automated estimation of facial expression intensity. This involves estimation of multiple output variables (facial action units --- AUs) that are structural…

cs.CV2017★ 2 cited

DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding

Dieu Linh Tran, Robert Walecki, Ognjen Rudovic +3

Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Vari…