25,268 research outputs found
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
Audio Deepfake Detection: A Survey
Audio deepfake detection is an emerging active topic. A growing number of
literatures have aimed to study deepfake detection algorithms and achieved
effective performance, the problem of which is far from being solved. Although
there are some review literatures, there has been no comprehensive survey that
provides researchers with a systematic overview of these developments with a
unified evaluation. Accordingly, in this survey paper, we first highlight the
key differences across various types of deepfake audio, then outline and
analyse competitions, datasets, features, classifications, and evaluation of
state-of-the-art approaches. For each aspect, the basic techniques, advanced
developments and major challenges are discussed. In addition, we perform a
unified comparison of representative features and classifiers on ASVspoof 2021,
ADD 2023 and In-the-Wild datasets for audio deepfake detection, respectively.
The survey shows that future research should address the lack of large scale
datasets in the wild, poor generalization of existing detection methods to
unknown fake attacks, as well as interpretability of detection results
Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches
Couples generally manage chronic diseases together and the management takes
an emotional toll on both patients and their romantic partners. Consequently,
recognizing the emotions of each partner in daily life could provide an insight
into their emotional well-being in chronic disease management. The emotions of
partners are currently inferred in the lab and daily life using self-reports
which are not practical for continuous emotion assessment or observer reports
which are manual, time-intensive, and costly. Currently, there exists no
comprehensive overview of works on emotion recognition among couples.
Furthermore, approaches for emotion recognition among couples have (1) focused
on English-speaking couples in the U.S., (2) used data collected from the lab,
and (3) performed recognition using observer ratings rather than partner's
self-reported / subjective emotions. In this body of work contained in this
thesis (8 papers - 5 published and 3 currently under review in various
journals), we fill the current literature gap on couples' emotion recognition,
develop emotion recognition systems using 161 hours of data from a total of
1,051 individuals, and make contributions towards taking couples' emotion
recognition from the lab which is the status quo, to daily life. This thesis
contributes toward building automated emotion recognition systems that would
eventually enable partners to monitor their emotions in daily life and enable
the delivery of interventions to improve their emotional well-being.Comment: PhD Thesis, 2022 - ETH Zuric
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