Transactions on Affective Computing

IEEE Transactions on Affective Computing (TAC) is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. Read the full scope of TAC

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TAC Seeks Editor-in-Chief for 2019-2020 Term

TAC seeks Editor-in-Chief applicants for a two-year term starting 1 January 2019, renewable for two years. Prospective candidates are asked to provide a complete curriculum vitae, a brief plan for the publication's future, and a letter of support from their institution or employer to Jennifer Carruth,, by 1 March 2018. Please click here for additional information.

From the October-December 2017 issue

Audio-Facial Laughter Detection in Naturalistic Dyadic Conversations

By Bekir Berker Turker, Yucel Yemez, T. Metin Sezgin, and Engin Erzin

Featured article thumbnail image We address the problem of continuous laughter detection over audio-facial input streams obtained from naturalistic dyadic conversations. We first present meticulous annotation of laughters, cross-talks and environmental noise in an audio-facial database with explicit 3D facial mocap data. Using this annotated database, we rigorously investigate the utility of facial information, head movement and audio features for laughter detection. We identify a set of discriminative features using mutual information-based criteria, and show how they can be used with classifiers based on support vector machines (SVMs) and time delay neural networks (TDNNs). Informed by the analysis of the individual modalities, we propose a multimodal fusion setup for laughter detection using different classifier-feature combinations. We also effectively incorporate bagging into our classification pipeline to address the class imbalance problem caused by the scarcity of positive laughter instances. Our results indicate that a combination of TDNNs and SVMs lead to superior detection performance, and bagging effectively addresses data imbalance. Our experiments show that our multimodal approach supported by bagging compares favorably to the state of the art in presence of detrimental factors such as cross-talk, environmental noise, and data imbalance.

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Editorials and Announcements


  • According to Clarivate Analytics' 2016 Journal Citation Report, TAC has an impact factor of 3.149.
  • Heartfelt congratulations are offered to Georgios N. Yannakakis and Julian Togelius, authors of "Experience-Driven Procedural Content Generation," who were presented with TAC's Most Influential Paper Award by Editor-in-Chief Björn W. Schuller at the 2015 6th AAAC Affective Computing and Intelligent Interaction Conference in Xi'An, P.R. China on 22 September 2015.


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