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Detecting depression using vocal, facial and semantic communication cues

October 15, 2016
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Conference Paper
Author:
James R. Williamson
…
Published in:
ACM Multimedia Conf. AVEC Workshop, 15-19 October 2016.
Topic:
biometrics
R&D area:
R&D group:

Summary

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In particular, MDD-induced neurophysiological changes are associated with a decline in dynamics and coordination of speech and facial motor control, while neurocognitive changes influence dialogue semantics. In this paper, biomarkers are derived from all of these modalities, drawing first from previously developed neurophysiologically motivated speech and facial coordination and timing features. In addition, a novel indicator of lower vocal tract constriction in articulation is incorporated that relates to vocal projection. Semantic features are analyzed for subject/avatar dialogue content using a sparse coded lexical embedding space, and for contextual clues related to the subject's present or past depression status. The features and depression classification system were developed for the 6th International Audio/Video Emotion Challenge (AVEC), which provides data consisting of audio, video-based facial action units, and transcribed text of individuals communicating with the human-controlled avatar. A clinical Patient Health Questionnaire (PHQ) score and binary depression decision are provided for each participant. PHQ predictions were obtained by fusing outputs from a Gaussian staircase regressor for each feature set, with results on the development set of mean F1=0.81, RMSE=5.31, and MAE=3.34. These compare favorably to the challenge baseline development results of mean F1=0.73, RMSE=6.62, and MAE=5.52. On test set evaluation, our system obtained a mean F1=0.70, which is similar to the challenge baseline test result. Future work calls for consideration of joint feature analyses across modalities in an effort to detect neurological disorders based on the interplay of motor, linguistic, affective, and cognitive components of communication.
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Summary

Major depressive disorder (MDD) is known to result in neurophysiological and neurocognitive changes that affect control of motor, linguistic, and cognitive functions. MDD's impact on these processes is reflected in an individual's communication via coupled mechanisms: vocal articulation, facial gesturing and choice of content to convey in a dialogue. In...

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Detecting depression using vocal, facial and semantic communication cues

Sparse-coded net model and applications

September 13, 2016
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Conference Paper
Author:
Youngjune L. Gwon
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Published in:
2016 IEEE Int. Workshop on Machine Learning for Signal Processing, 13-16 September 2016.
Topic:
artificial intelligence
R&D area:
R&D group:

Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task perform well, we argue that sparse coding should also be built as a holistic learning unit optimizing on the supervised task objectives more explicitly. In this paper, we propose sparse-coded net, a feedforward model that integrates sparse coding and task-driven output layers, and describe training methods in detail. After pretraining a sparse-coded net via semi-supervised learning, we optimize its task-specific performance in a novel backpropagation algorithm that can traverse nonlinear feature pooling operators to update the dictionary. Thus, sparse-coded net can be applied to supervised dictionary learning. We evaluate sparse-coded net with classification problems in sound, image, and text data. The results confirm a significant improvement over semi-supervised learning as well as superior classification performance against deep stacked autoencoder neural network and GMM-SVM pipelines in small to medium-scale settings.
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Summary

As an unsupervised learning method, sparse coding can discover high-level representations for an input in a large variety of learning problems. Under semi-supervised settings, sparse coding is used to extract features for a supervised task such as classification. While sparse representations learned from unlabeled data independently of the supervised task...

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Sparse-coded net model and applications

Language recognition via sparse coding

September 8, 2016
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Conference Paper
Author:
Youngjune L. Gwon
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Published in:
INTERSPEECH 2016: 16th Annual Conf. of the Int. Speech Communication Assoc., 8-12 September 2016.
Topic:
language recognition
R&D area:
R&D group:

Summary

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a speech utterance while learning basis vectors for language models. Differentiated from existing approaches in sparse representation classification, we introduce a maximum a posteriori (MAP) adaptation scheme based on online learning that further optimizes the discriminative quality of sparse-coded speech features. We empirically validate the effectiveness of our approach using the NIST LRE 2015 dataset.
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Summary

Spoken language recognition requires a series of signal processing steps and learning algorithms to model distinguishing characteristics of different languages. In this paper, we present a sparse discriminative feature learning framework for language recognition. We use sparse coding, an unsupervised method, to compute efficient representations for spectral features from a...

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Language recognition via sparse coding

Blind signal classification via sparse coding

April 10, 2016
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Conference Paper
Author:
Youngjune L. Gwon
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Published in:
IEEE Int. Conf. Computer Communications, IEEE INFOCOM 2016, 10-15 April 2016.
Topic:
signal processing
R&D area:
R&D group:

Summary

We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level features by computing the maximal similarity between an unknown received signal against an overcomplete dictionary of matched filter templates. Such dictionary can be either generated or trained in an unsupervised fashion from signal examples labeled with no ground truths. The computed sparse code then is applied to train SVM classifiers to discriminate RF signals. As a result, the proposed approach can achieve blind signal classification that requires no prior knowledge (e.g., MCS, pulse shaping) about the signals present in an arbitrary RF channel. Since modulated RF signals undergo pulse shaping to aid the matched filter detection by a receiver for the same radio protocol, our method can exploit variability in relative similarity against the dictionary atoms as the key discriminating factor for SVM. We present an empirical validation of our approach. The results indicate that we can separate different classes of digitally modulated signals from blind sampling with 70.3% recall and 24.6% false alarm at 10 dB SNR. If a labeled dataset were available for supervised classifier training, we can enhance the classification accuracy to 87.8% recall and 14.1% false alarm.
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Summary

We propose a novel RF signal classification method based on sparse coding, an unsupervised learning method popular in computer vision. In particular, we employ a convolutional sparse coder that can extract high-level features by computing the maximal similarity between an unknown received signal against an overcomplete dictionary of matched filter...

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Blind signal classification via sparse coding

Competing cognitive resilient networks

March 1, 2016
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Journal Article
Author:
Siamak Dastangoo
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Published in:
IEEE Trans. Cognit. Commun. and Netw., Vol. 2, No. 1, March 2016, pp. 95-109.
Topic:
networking
R&D area:
R&D group:

Summary

We introduce competing cognitive resilient network (CCRN) of mobile radios challenged to optimize data throughput and networking efficiency under dynamic spectrum access and adversarial threats (e.g., jamming). Unlike the conventional approaches, CCRN features both communicator and jamming nodes in a friendly coalition to take joint actions against hostile networking entities. In particular, this paper showcases hypothetical blue force and red force CCRNs and their competition for open spectrum resources. We present state-agnostic and stateful solution approaches based on the decision theoretic framework. The state-agnostic approach builds on multiarmed bandit to develop an optimal strategy that enables the exploratory-exploitative actions from sequential sampling of channel rewards. The stateful approach makes an explicit model of states and actions from an underlying Markov decision process and uses multiagent Q-learning to compute optimal node actions. We provide a theoretical framework for CCRN and propose new algorithms for both approaches. Simulation results indicate that the proposed algorithms outperform some of the most important algorithms known to date.
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Summary

We introduce competing cognitive resilient network (CCRN) of mobile radios challenged to optimize data throughput and networking efficiency under dynamic spectrum access and adversarial threats (e.g., jamming). Unlike the conventional approaches, CCRN features both communicator and jamming nodes in a friendly coalition to take joint actions against hostile networking entities...

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Competing cognitive resilient networks

Fast online learning of antijamming and jamming strategies

December 6, 2015
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Conference Paper
Author:
Youngjune L. Gwon
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Published in:
2015 IEEE Global Communications Conf., 6-10 December 2015.
Topic:
communications
R&D area:
R&D group:

Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance. This paper addresses a harder class of problems where channel rewards are time-varying such that learning based on stochastic assumptions cannot guarantee the optimal performance. This new problem is important because an intelligent adversary will likely introduce dynamic changepoints, which can make our previous approaches ineffective. We propose a new, faster learning algorithm using online convex programming that is computationally simpler and stateless. According to our empirical results, the new algorithm can almost instantly find an optimal strategy that achieves the best steady-state channel rewards.
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Summary

Competing Cognitive Radio Network (CCRN) coalesces communicator (comm) nodes and jammers to achieve maximal networking efficiency in the presence of adversarial threats. We have previously developed two contrasting approaches for CCRN based on multi-armed bandit (MAB) and Qlearning. Despite their differences, both approaches have shown to achieve optimal throughput performance...

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Fast online learning of antijamming and jamming strategies

Optimizing media access strategy for competing cognitive radio networks

December 9, 2013
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Conference Paper
Author:
Youngjune L. Gwon
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Published in:
GLOBECOM 2013: 2013 IEEE Global Communications Conf., 9-13 December 2013.
Topic:
communications
R&D area:
R&D group:

Summary

This paper describes an adaptation of cognitive radio technology for tactical wireless networking. We introduce Competing Cognitive Radio Network (CCRN) featuring both communicator and jamming cognitive radio nodes that strategize in taking actions on an open spectrum under the presence of adversarial threats. We present the problem in the Multi-armed Bandit (MAB) framework and develop the optimal media access strategy consisting of mixed communicator and jammer actions in a Bayesian setting for Thompson sampling based on extreme value theory. Empirical results are promising that the proposed strategy seems to outperform Lai & Robbins and UCB, some of the most important MAB algorithms known to date.
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Summary

This paper describes an adaptation of cognitive radio technology for tactical wireless networking. We introduce Competing Cognitive Radio Network (CCRN) featuring both communicator and jamming cognitive radio nodes that strategize in taking actions on an open spectrum under the presence of adversarial threats. We present the problem in the Multi-armed...

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Optimizing media access strategy for competing cognitive radio networks

Competing Mobile Network Game: embracing antijamming and jamming strategies with reinforcement learning

October 14, 2013
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Journal Article
Author:
Youngjune L. Gwon
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Published in:
2013 IEEE Conf. on Communications and Network Security (CNS), 14-16 October 2013, pp. 28-36.
Topic:
communications
R&D area:
R&D group:

Summary

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-for Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.
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Summary

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework...

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Competing Mobile Network Game: embracing antijamming and jamming strategies with reinforcement learning
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