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Large-Scale Unsupervised Pre-Training For End-To-End Spoken Language Understanding
End-to-end Spoken Language Understanding (SLU) is proposed to infer the semantic meaning directly from audio features without intermediate text representation. In this paper, we explore unsupervised pre-training for End-to-end SLU models by learning repre
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A New Sampling Scheme For Distributed Blind Spectrum Sensing Using Energy Detectors
In this paper, we study the problem of blind spectrum sensing by exploring signal sampling at each cognitive radio (CR) in a distributed cognitive radio network. Specifically, a new cooperative sampling scheme is proposed to deal with the challenge of unk
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Formulating Divergence Framework For Multiclass Motor Imagery Eeg Brain Computer Interface
The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonaliza
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Balancing Rates And Variance Via Adaptive Batch-Sizes In First-Order Stochastic Optimization
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-sizes is required for exact a
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Sequence-To-Sequence Labanotation Generation Based On Motion Capture Data
Labanotation is an important notation system for recording dances. Automatically generating Labanotation scores from motion capture data has attracted more interest in recent years. Current methods usually focus on individual movement segments and generat
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Energy Disaggregation Using Fractional Calculus
Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by one smartmeter. In this article we introduce the use of fractional calculus in the Non-Intr
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An Empirical Bayes Approach To Partially Labeled And Shuffled Data Sets
This work outlines a method for an application of empirical Bayes in the setting of semi-supervised learning. That is, we consider a scenario in which the training set is partially or entirely unlabeled. In addition to the missing labels, we also consider
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Resilient Distributed Recovery Of Large Fields
This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a large spatially distributed field. An adversary corrupts some of the measuremen
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Hybrid Active Contour Driven By Double-Weighted Signed Pressure Force For Image Segmentation
In this paper, we proposed a novel hybrid active contour driven by double-weighted signed pressure force method for image segmentation. First, the Legendre polynomials and global information are integrated into the signed pressure force (SPF) function and
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Dilated Convolutional Neural Networks For Panoramic Image Saliency Prediction
Saliency prediction is an important way to understand human?s behavior and has a wide range of applications. Although lots of algorithms have been designed to predict saliency for planar images, there are few works for 360? images. In this paper, we propo
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Non-Experts Or Experts? Statistical Analyses Of Mos Using Dsis Method
In image quality assessments, the results of subjective evaluation experiments that use the double-stimulus impairment scale (DSIS) method are often expressed in terms of the mean opinion score (MOS), which is the average score of all subjects for each te
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Joint Semi-Supervised Feature Auto-Weighting And Classification Model For Eeg-Based Cross-Subject Sleep Quality Evaluation
Measuring the sleep quality is important or even crucial for people who are engaged in dangerous jobs such as the highspeed train drivers. Since the scalp EEG data are generated by the neural activities of the brain cortex, it is collected from subjects w
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A Generalized Framework For Domain Adaptation Of Plda In Speaker Recognition
This paper proposes a generalized framework for domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA) in speaker recognition. It not only includes several existing supervised and unsupervised domain adaptation methods but also makes possi
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Variable Metric Proximal Gradient Method With Diagonal Barzilai-Borwein Stepsize
This paper proposes an adaptive metric selection strategy called diagonal Barzilai-Borwein (DBB) stepsize for the popular Variable Metric Proximal Gradient (VM-PG) algorithm. The proposed approach better captures the local geometry of the problem while ke
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Learning Spectral-Spatial Prior Via 3Ddncnn For Hyperspectral Image Deconvolution
Hyperspectral image (HSI) deconvolution is an ill-posed problem aiming at recovering sharp images with tens or hundreds of spectral channels from blurred and noisy observations. In order to successfully conduct the deconvolution, proper priors are require
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Versatile Video Coding And Super-Resolution For Efficient Delivery Of 8K Video With 4K Backward-Compatibility
In this paper, we propose, through an objective study, to compare and evaluate the performance of different coding approaches allowing the delivery of an 8K video signal with 4K backward-compatibility on broadcast networks. Presented approaches include si
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A Framework For Parameters Estimation Of Image Operator Chain
Currently, many effective techniques have been proposed to estimate the parameters of tampering operations. Most of them consider the situation that an image is tampered by only one operation. However, multiple manipulation operations are always used to t
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A Stacked-Autoencoder Based End-To-End Learning Framework For Decode-And-Forward Relay Networks
In this work, we study an end-to-end deep learning (DL)-based constellation design for decode-and-forward (DF) relay network. Firstly, we study both the one-way (OW) and two-way (TW) relaying by interpreting DF relay networks as stacked autoencoders, unde
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Al2: Progressive Activation Loss For Learning General Representations In Classification Neural Networks
The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting
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Disentangling Controllable Object Through Video Prediction Improves Visual Reinforcement Learning
In many vision-based reinforcement learning (RL) problems, the agent controls a movable object in its visual field, e.g., the player?s avatar in video games and the robotic arm in visual grasping and manipulation. Leveraging action-conditioned video predi
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Primal-Dual Stochastic Subgradient Method For Log-Determinant Optimization
The log-determinant optimization problem with general matrix constraints arises in many applications. The log-determinant term hampers the scalability of existing methods. This paper proposes a highly efficient stochastic method that has time complexity O
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Parameter Estimation Of In-City Frontal Rainfall Propagation
Modern infrastructures support smart-city operations based on short millimeter-waves wireless links connected by a dense network. These links are sensitive to hydrometeors, and their signals attenuated by rain. In this study, we demonstrate that standard
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Optimal Transport Structure Of Cyclegan For Unsupervised Learning For Inverse Problems
Optimal transport (OT) is a mathematical theory that can provide a tool how to transfer one measure to another measure at minimal cost, thus serve another framework for computer vision tasks of image processing without reference. Cycle-consistent generati
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Secure Identification For Gaussian Channels
New applications in modern communications are demanding robust and ultra-reliable low latency information exchange such as machine-to-machine and human-to-machine communications. For many of these applications, the identification approach of Ahlswede and
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Mspnet: Multi-Supervised Parallel Network For Crowd Counting
Crowd counting has a wide range of applications such as video surveillance and public safety. Many existing methods only focus on improving the accuracy of counting but ignore the importance of density maps. It?s no doubt that a high-quality density map c
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Color Stabilization For Multi-Camera Light-Field Imaging
By capturing a more complete rendition of scene light than standard 2D cameras, light-field technology represents an important step towards closing the gap between live action cinematography and computer graphics. Light-field cameras accomplish this by si
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Layer-Normalized Lstm For Hybrid-Hmm And End-To-End Asr
Training deep neural networks is often challenging in terms of training stability. It often requires careful hyperparameter tuning or a pretraining scheme to converge. Layer normalization (LN) has shown to be a crucial ingredient in training deep encoder-
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Polyphonic sound event detection and direction-of-arrival estimation require different input features from audio signals. While sound event detection mainly relies on time-frequency patterns, direction-of-arrival estimation relies on magnitude or phase di
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Adversarial Anomaly Detection For Marked Spatio-Temporal Streaming Data
Spatio-temporal event data are becoming increasingly commonplace in a wide variety of applications, such as electronic transaction records, social network data, and crime data. How to efficiently detect anomalies in these dynamic systems using these strea
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Playing Technique Recognition By Joint Time–Frequency Scattering
Playing techniques are important expressive elements in music signals. In this paper, we propose a recognition system based on the joint time?frequency scattering transform (jTFST) for pitch evolution-based playing techniques (PETs), a group of playing te
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Lightdet: A Lightweight And Accurate Object Detection Network
The extensive computational burden limits the usage of accurate but complex object detectors in resource-bounded scenarios. In this paper, we present a lightweight object detector, named LightDet, to address this dilemma. We design a lightweight backbone
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Auxiliary Capsules For Natural Language Understanding
Lately, joint training of intent detection and slot filling has become the best performing approach on the field of Natural Language Understanding (NLU). In this work we explore the combination of the newly introduced capsule network, in a multi-task lear
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Sequential Vessel Trajectory Identification Using Truncated Viterbi Algorithm
In this work, we propose a novel classification algorithm that used to classify vessel data points into different trajectories. The algorithm is a truncated version of the Viterbi Algorithm. A physical model utilizing the observation information is used t
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Efficient Belief Propagation For Graph Matching
In this short note we derive a novel belief propagation algorithm for graph matching and we numerically evaluate it in the context of matching random graphs. The derived algorithm has a lower asymptotic time-complexity without significantly compromising t
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Volume Reconstruction For Light Field Microscopy
Light Field Microscopy is a 3D imaging technique that captures volumetric information in a single snapshot. It is appealing in microscopy because of its simple implementation and the peculiarity that it is much faster than methods involving scanning. Howe
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Generating Synthetic Audio Data For Attention-Based Speech Recognition Systems
Recent advances in text-to-speech (TTS) led to the development of flexible multi-speaker end-to-end TTS systems. We extend state-of-the-art attention-based automatic speech recognition (ASR) systems with synthetic audio generated by a TTS system trained o
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Favorable Propagation And Linear Multiuser Detection For Distributed Antenna Systems
Cell-free MIMO, employing distributed antenna systems (DAS), is a promising approach to deal with the capacity crunch of next generation wireless communications. In this paper, we consider a wireless network with transmit and receive antennas distributed
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Optimal Power Flow Using Graph Neural Networks
Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is m
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Sequential Methods For Detecting A Change In The Distribution Of An Episodic Process
A new class of stochastic processes called episodic processes is introduced to model the statistical regularity of data observed in several applications in cyberphysical systems, neuroscience, and medicine. Algorithms are proposed to detect a change in th
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Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models
This paper aims at providing an original Riemannian geometry to derive robust covariance matrix estimators in spiked models (i.e. when the covariance matrix has a low-rank plus identity structure). The considered geometry is the one induced by the product
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Atrial Fibrillation Risk Prediction From Electrocardiogram And Related Health Data With Deep Neural Network
Electrocardiography (ECG) is a widely used tool for studying and diagnosing the heart diseases. Atrial fibrillation (AF) is an irregular and often rapid heart rate that can increase the risk of strokes, heart failure and other heart-related complications.
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Phylogenetic Minimum Spanning Tree Reconstruction Using Autoencoders
The history of a shared and re-posted multimedia content can be reconstructed by analyzing the mutual relations between all of its near-duplicate copies and solving a minimum spanning tree (MST) problem, as shown by multimedia phylogeny research field. Un
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Deep James-Stein Neural Networks For Brain-Computer Interfaces
Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness
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Speech Recognition Model Compression
Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-inten
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Spatial Gating Strategies For Graph Recurrent Neural Networks
Graph Recurrent Neural Networks (GRNNs) are a neural network architecture devised to learn from graph processes, which are time sequences of graph signals. Similarly to traditional recurrent neural networks, GRNNs experience the problem of vanishing/explo
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Deep Learning Based Prediction Of Hypernasality For Clinical Applications
Hypernasality refers to the perception of excessive nasal resonance during the production of oral sounds. Existing methods for automatic assessment of hypernasality from speech are based on machine learning models trained on disordered speech databases ra
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Speech-Driven Facial Animation Using Polynomial Fusion Of Features
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decod
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Secl-Umons Database For Sound Event Classification And Localization
We introduce the SECL-UMons dataset for sound event classification and localization in the context of office environments. The multichannel dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset
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Disentangled Speech Embeddings Using Cross-Modal Self-Supervision
The objective of this paper is to learn representations of speaker identity without access to manually annotated data. To do so, we develop a self-supervised learning objective that exploits the natural cross-modal synchrony between faces and audio in vid