40,610 research outputs found
A dataset of continuous affect annotations and physiological signals for emotion analysis
From a computational viewpoint, emotions continue to be intriguingly hard to
understand. In research, direct, real-time inspection in realistic settings is
not possible. Discrete, indirect, post-hoc recordings are therefore the norm.
As a result, proper emotion assessment remains a problematic issue. The
Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as
it focusses on real-time continuous annotation of emotions, as experienced by
the participants, while watching various videos. For this purpose, a novel,
intuitive joystick-based annotation interface was developed, that allowed for
simultaneous reporting of valence and arousal, that are instead often annotated
independently. In parallel, eight high quality, synchronized physiological
recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA),
respiration and skin temperature. The dataset consists of the physiological and
annotation data from 30 participants, 15 male and 15 female, who watched
several validated video-stimuli. The validity of the emotion induction, as
exemplified by the annotation and physiological data, is also presented.Comment: Dataset available at:
https://rmc.dlr.de/download/CASE_dataset/CASE_dataset.zi
A generic news story segmentation system and its evaluation
The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis
Factor analysis aims to determine latent factors, or traits, which summarize
a given data set. Inter-battery factor analysis extends this notion to multiple
views of the data. In this paper we show how a nonlinear, nonparametric version
of these models can be recovered through the Gaussian process latent variable
model. This gives us a flexible formalism for multi-view learning where the
latent variables can be used both for exploratory purposes and for learning
representations that enable efficient inference for ambiguous estimation tasks.
Learning is performed in a Bayesian manner through the formulation of a
variational compression scheme which gives a rigorous lower bound on the log
likelihood. Our Bayesian framework provides strong regularization during
training, allowing the structure of the latent space to be determined
efficiently and automatically. We demonstrate this by producing the first (to
our knowledge) published results of learning from dozens of views, even when
data is scarce. We further show experimental results on several different types
of multi-view data sets and for different kinds of tasks, including exploratory
data analysis, generation, ambiguity modelling through latent priors and
classification.Comment: 49 pages including appendi
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