784 research outputs found
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results
Humour is a substantial element of human affect and cognition. Its automatic
understanding can facilitate a more naturalistic human-device interaction and
the humanisation of artificial intelligence. Current methods of humour
detection are solely based on staged data making them inadequate for
'real-world' applications. We address this deficiency by introducing the novel
Passau-Spontaneous Football Coach Humour (Passau-SFCH) dataset, comprising of
about 11 hours of recordings. The Passau-SFCH dataset is annotated for the
presence of humour and its dimensions (sentiment and direction) as proposed in
Martin's Humor Style Questionnaire. We conduct a series of experiments,
employing pretrained Transformers, convolutional neural networks, and
expert-designed features. The performance of each modality (text, audio, video)
for spontaneous humour recognition is analysed and their complementarity is
investigated. Our findings suggest that for the automatic analysis of humour
and its sentiment, facial expressions are most promising, while humour
direction can be best modelled via text-based features. The results reveal
considerable differences among various subjects, highlighting the individuality
of humour usage and style. Further, we observe that a decision-level fusion
yields the best recognition result. Finally, we make our code publicly
available at https://www.github.com/EIHW/passau-sfch. The Passau-SFCH dataset
is available upon request.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible (Major Revision
Facial affect "in the wild": a survey and a new database
Well-established databases and benchmarks have been developed in the past 20 years for automatic facial behaviour analysis. Nevertheless, for some important problems regarding analysis of facial behaviour, such as (a) estimation of affect in a continuous dimensional space (e.g., valence and arousal) in videos displaying spontaneous facial behaviour and (b) detection of the activated facial muscles (i.e., facial action unit detection), to the best of our knowledge, well-established in-the-wild databases and benchmarks do not exist. That is, the majority of the publicly available corpora for the above tasks contain samples that have been captured in controlled recording conditions and/or captured under a very specific milieu. Arguably, in order to make further progress in automatic understanding of facial behaviour, datasets that have been captured in in the-wild and in various milieus have to be developed. In this paper, we survey the progress that has been recently made on understanding facial behaviour in-the-wild, the datasets that have been developed so far and the methodologies that have been developed, paying particular attention to deep learning techniques for the task. Finally, we make a significant step further and propose a new comprehensive benchmark for training methodologies, as well as assessing the performance of facial affect/behaviour analysis/ understanding in-the-wild. To the best of our knowledge, this is the first time that such a benchmark for valence and arousal "in-the-wild" is presente
The Emotional Impact of Audio - Visual Stimuli
Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and audio features through machine-learning algorithms. The goal is to maximize accuracy, with the hope that each feature will bring useful information to the table.
We depart from this approach to quantify how different modalities such as visual, audio, and text description information can aid in the understanding affect. To that end, we train independent models for visual, audio and text description. Each are convolutional neural networks paired with support vector machines to classify valence and arousal. We also train various ensemble models that combine multi-modal information with the hope that the information from independent modalities benefits each other.
We find that our visual network alone achieves state-of-the-art valence classification accuracy and that our audio network, when paired with our visual, achieves competitive results on arousal classification. Each network is much stronger on one metric than the other. This may lead to more sophisticated multimodal approaches to accurately identifying affect in video data. This work also contributes to induced emotion classification by augmenting existing sizable media datasets and providing a robust framework for classifying the same
Multimodal Speech Emotion Recognition Using Audio and Text
Speech emotion recognition is a challenging task, and extensive reliance has
been placed on models that use audio features in building well-performing
classifiers. In this paper, we propose a novel deep dual recurrent encoder
model that utilizes text data and audio signals simultaneously to obtain a
better understanding of speech data. As emotional dialogue is composed of sound
and spoken content, our model encodes the information from audio and text
sequences using dual recurrent neural networks (RNNs) and then combines the
information from these sources to predict the emotion class. This architecture
analyzes speech data from the signal level to the language level, and it thus
utilizes the information within the data more comprehensively than models that
focus on audio features. Extensive experiments are conducted to investigate the
efficacy and properties of the proposed model. Our proposed model outperforms
previous state-of-the-art methods in assigning data to one of four emotion
categories (i.e., angry, happy, sad and neutral) when the model is applied to
the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.Comment: 7 pages, Accepted as a conference paper at IEEE SLT 201
Automatic Emotion Recognition: Quantifying Dynamics and Structure in Human Behavior.
Emotion is a central part of human interaction, one that has a huge influence on its overall tone and outcome. Today's human-centered interactive technology can greatly benefit from automatic emotion recognition, as the extracted affective information can be used to measure, transmit, and respond to user needs. However, developing such systems is challenging due to the complexity of emotional expressions and their dynamics in terms of the inherent multimodality between audio and visual expressions, as well as the mixed factors of modulation that arise when a person speaks. To overcome these challenges, this thesis presents data-driven approaches that can quantify the underlying dynamics in audio-visual affective behavior. The first set of studies lay the foundation and central motivation of this thesis. We discover that it is crucial to model complex non-linear interactions between audio and visual emotion expressions, and that dynamic emotion patterns can be used in emotion recognition. Next, the understanding of the complex characteristics of emotion from the first set of studies leads us to examine multiple sources of modulation in audio-visual affective behavior. Specifically, we focus on how speech modulates facial displays of emotion. We develop a framework that uses speech signals which alter the temporal dynamics of individual facial regions to temporally segment and classify facial displays of emotion. Finally, we present methods to discover regions of emotionally salient events in a given audio-visual data. We demonstrate that different modalities, such as the upper face, lower face, and speech, express emotion with different timings and time scales, varying for each emotion type. We further extend this idea into another aspect of human behavior: human action events in videos. We show how transition patterns between events can be used for automatically segmenting and classifying action events. Our experimental results on audio-visual datasets show that the proposed systems not only improve performance, but also provide descriptions of how affective behaviors change over time. We conclude this dissertation with the future directions that will innovate three main research topics: machine adaptation for personalized technology, human-human interaction assistant systems, and human-centered multimedia content analysis.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133459/1/yelinkim_1.pd
- …