H
Hichem Sahli
Researcher at Vrije Universiteit Brussel
Publications - 371
Citations - 3980
Hichem Sahli is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Image segmentation & Computer science. The author has an hindex of 26, co-authored 331 publications receiving 3310 citations. Previous affiliations of Hichem Sahli include VU University Amsterdam & Northwestern Polytechnical University.
Papers
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Proceedings ArticleDOI
Multimodal Affective Dimension Prediction Using Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks
TL;DR: A Deep Bidirectional Long Short-Term Memory Recurrent Neural Network based multimodal affect prediction framework, in which the initial predictions from the single modalities via the DBLSTM-RNNs are firstly smoothed with Gaussian smoothing, then input into a second layer of DBLstM- RNN for the final prediction of affective state.
Proceedings ArticleDOI
Hybrid Deep Neural Network--Hidden Markov Model (DNN-HMM) Based Speech Emotion Recognition
Longfei Li,Yong Zhao,Dongmei Jiang,Yanning Zhang,Fengna Wang,Isabel Gonzalez,Enescu Valentin,Hichem Sahli +7 more
TL;DR: Experimental results show that when the numbers of the hidden layers as well hidden units are properly set, the DNN could extend the labeling ability of GMM-HMM.
Proceedings ArticleDOI
Multimodal Measurement of Depression Using Deep Learning Models
TL;DR: The proposed depression recognition framework obtains very promising accuracy, with the root mean square error (RMSE) as 4.653, mean absolute error (MAE) as 3.980 on the development set, and RMSE as 5.974 on the test set.
Proceedings ArticleDOI
Decision Tree Based Depression Classification from Audio Video and Language Information
TL;DR: A decision tree is constructed according to the distribution of the multimodal prediction of PHQ-8 scores and participants' characteristics obtained via the analysis of the transcript files of the participants to improve the recognition accuracy of the Depression Classification Sub-Challenge (DCC) of the AVEC 2016.
Journal ArticleDOI
Investigation of Time–Frequency Features for GPR Landmine Discrimination
TL;DR: Time-frequency features of an ultrawideband (UWB) target response for the discrimination between buried landmines and other objects are investigated and the obtained results define the best features and conditions when the landmine discrimination is successful.