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Deep learning technologies have been successfully applied to acoustic emotion recognition lately. In this work, we propose to apply multi-task learning for acoustic emotion recognition based on the Deep Belief Network (DBN) framework. We treat the categorical emotion recognition task as the major task. For the secondary task, we leverage two continuous labels, valence and activation. Two strategies...
This work addresses the problem of constructing an effective training set at minimal labeling cost by selecting some images to build a subset from the whole database. This problem occurs in situations that the number of categories is large or the cost of obtaining labeled images is extremely high, because the images selected by uniform sampling do not reflect the desired training distribution and...
With the permeation of Web 2.0, large-scale user contributed images with tags are easily available on social websites. How to align these social tags with image regions is a challenging task while no additional human intervention is considered, but a valuable one since the alignment can provide more detailed image semantic information and improve the accuracy of image retrieval. To this end, we propose...
This study proposes several categories of stimulus types under the visual oddball speller paradigm with application to brain-computer interface (BCI). Motion (including translation and rotation), zoom in/out, pattern rotation and sharpening types are tested and analysed. Results show that rotation and zooming type could obtain comparative or higher accuracies as well as information transfer rates...
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