Content Gazette - November 2020 - 17

Call for Papers
IEEE Signal Processing Society
IEEE SIGNAL PROCESSING MAGAZINE
Special Issue on Intelligent Signal Processing for Affective Computing
Affective Computing has matured over its roughly two-and-a-half decades coming closer than ever to the point of usage at
large. Once entering into everyday usage, Affective Computing has the potential to massively change how we interact with
computing and robotic devices: They will be able to respond more appropriately to our emotions and moods, and able to show
signs of empathy through mimicry, but may also use affective information for retrieval or their own creativity. Affective
Computing becoming truly robust also has the potential to massively change mental health care, once computing systems are
able to monitor our wellbeing or potential depression, or, just as a further example, children's development. In most if not
all of these and manifold further applications, however, reliable assessment of affect and affective behavior is key. A major
breakthrough in the field - as has been the case in many related intelligent signal processing problems - came with the advent
and increasing usage of deep learning and further novel techniques of machine intelligence for signal processing. Likewise,
end-to-end learning from the raw signal or shallow time-frequency representations and more general unsupervised
representation learning are frequently encountered if not omnipresent in today's literature on Affective Computing. In addition,
generative adversarial approaches and transfer learning exploiting pre-trained neural networks is on the rise, going as far as
using image-pretrained convolutional networks for the representation of audio or physiology data. The latter is triggered by the
field's ever-dominating bottleneck of sufficient training data. While such approaches led to an impressive number of successes
in boosting performances, it came at the price of 1) a major change in the processing of affective signals in a 2) often reduced
transparency in the signal processing and decision-making parts. The lower explainability can be attributed to self-learnt,
generated, and transferred representations and increasing data-injection both into the signal representation, but also signal preprocessing parts, such as source separation or signal restauration and enhancement.
This Special Issue seeks to offer broad coverage of Intelligent Signal Processing for Affective Computing with an emphasis on
techniques that focus on machine learning for signal pre-processing and signal representation, their combination with modelbased and conventional approaches and related arising questions. Submissions of comprehensive overviews of methodological
advances are encouraged, as well as more application-oriented contributions. Articles should provide new insights to the
problem that is of interest to many areas of signal processing, explain complex concepts and subjects in a way that is easily
accessible to the general, non-expert audience, and offer the value of bringing the magazine's readers quickly to a new area.
Topics of interest include (but are not limited to):
* Intelligent Affective Signal Processing and combination with model-based approaches in Affective Computing
* Adversarial Affective Signal Processing, Transfer, and Automatic and Reinforced Learning for Affective Signals
* Intelligent Multimodal/-sensorial Affective Signal Fusion
* Context-embedding in Affective Signal Processing
* Explainable Affective Signal Processing, Trustability, and Human Acceptability of Affective Signal Processing
* Applications (e.g., in digital health, psychology and psychiatry, education, edutainment, HCI/HRI, security)
Submission Process
The Special Issue seeks to offer broad coverage of the field including most recent developments in both theory and
applications. Submissions of comprehensive overviews of methodological advances are strongly encouraged, as well as papers
dealing with new and emerging applications. All submissions will be peer reviewed according to the IEEE and Signal
Processing Society guidelines. Submitted articles should not have been published or be under review elsewhere. Manuscripts
should be submitted online at http://mc.manuscriptcentral.com/sps-ieee using the Manuscript Central interface, see
http://www.signalprocessingsociety.org/publications/periodicals/spm/ for guidelines and information.
Important Dates
White papers (4 pages) due: November 1, 2020
Invitation notification: November 15, 2020
Full length manuscripts due: January 15, 2021
First review to authors: March 5, 2021

Revision due: May 1, 2021
Final decision: July 1, 2021
Final package due: August 3, 2021
Publish manuscript: November 1, 2021

Guest Editors:
Björn W. Schuller, Lead Guest Editor, Imperial College London, UK, schuller@ieee.org
Rosalind W. Picard, MIT Media Lab, USA, picard@media.mit.edu
Elisabeth André, University of Augsburg, Germany, andre@informatik.uni-augsburg.de
Jonathan Gratch, University of Southern California, USA, gratch@ict.usc.edu
Jianhua Tao, Chinese Academy of Sciences, China, jhtao@nlpr.ia.ac.cn

www.signalprocessingsociety.org

17

NOVEMBER 2020
http://mc.manuscriptcentral.com/sps-ieee http://www.signalprocessingsociety.org/publications/periodicals/spm/ http://www.signalprocessingsociety.org
Content Gazette - November 2020

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