Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Combining eye movements and collaborative filtering for proactive information retrieval
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Interaction Design: Beyond Human Computer Interaction
Interaction Design: Beyond Human Computer Interaction
Proceedings of the 24th international conference on Machine learning
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
GaZIR: gaze-based zooming interface for image retrieval
Proceedings of the 2009 international conference on Multimodal interfaces
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Galvanic skin response-derived bookmarking of an audio stream
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Emotion assessment: arousal evaluation using EEG's and peripheral physiological signals
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
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We study ways of automatically inferring the level of attention a user is paying to auditory content, with applications for example in automatic podcast highlighting and auto-pause, as well as in a selection mechanism in auditory interfaces. In particular, we demonstrate how the level of attention can be inferred in an unsupervised fashion, without requiring any labeled training data. The approach is based on measuring the (generalized) correlation or synchrony between the auditory content and physiological signals reflecting the state of the user. We hypothesize that the synchrony is higher when the user is paying attention to the content, and show empirically that the level of attention can indeed be inferred based on the correlation. In particular, we demonstrate that the novel method of time-varying Bayesian canonical correlation analysis gives unsupervised prediction accuracy comparable to having trained a supervised Gaussian process regression with labeled training data recorded from other users.