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Object Detection With Color And Depth Images With Multi-Reduced Region Proposal Network And Multi-Pooling
Object detection technology has received increasing research attention with recent developments in automation technology. Most studies in this field, however, use RGB images as input to deep-learning classifiers, and they rarely use depth information. So,
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Subject Transfer Framework Based On Source Selection And Semi-Supervised Style Transfer Mapping For Semg Pattern Recognition
To construct subject-specific feature extractors and classifiers for a new subject using pooled datasets, overcoming inter-subject variabilities is required. In this study, we investigate the efficiency of the proposed subject transfer framework, which ap
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Using Vaes And Normalizing Flows For One-Shot Text-To-Speech Synthesis Of Expressive Speech
We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a
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A Novel Two-Pathway Encoder-Decoder Network For 3D Face Reconstruction
3D Morphable Model(3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address th
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Mixup-Breakdown: A Consistency Training Method For Improving Generalization Of Speech Separation Models
Deep-learning based speech separation models confront poor generalization problem that even the state-of-the-art models could abruptly fail when evaluating them in mismatch conditions. To address this problem, we propose an easy-to-implement yet effective
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A Whiteness Test Based On The Spectral Measure Of Large Non-Hermitian Random Matrices
In the context of multivariate time series, a whiteness test against an MA(1) correlation model is proposed. This test is built on the eigenvalue distribution (spectral measure) of the non-Hermitian one-lag sample autocovariance matrix, instead of its sin
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Neural Coding Strategies For Event-Based Vision Data
Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Pre
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Online Channel Estimation For Hybrid Beamforming Architectures
Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) ar
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Tensor Decomposition-Based Beamspace Esprit Algorithm For Multidimensional Harmonic Retrieval
Beamspace processing is an efficient and commonly used approach in harmonic retrieval (HR). In the beamspace, measurements are obtained by linearly transforming the sensing data, thereby achieving a compromise between estimation accuracy and system comple
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Preservation Of Anomalous Subgroups On Variational Autoencoder Transformed Data
We investigate the effect of variational autoencoder (VAE) based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group?s
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Fine-Grained Giant Panda Identification
The image-based fine-grained identification of individual giant pandas (Ailuropoda melanoleuca) is an emerging technology, and it is extraordinarily challenging due to the extremely subtle visual differences between individual giant pandas and limited ann
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Addressing The Polysemy Problem In Language Modeling With Attentional Multi-Sense Embeddings
Neural network language models have gained considerable popularity due to their promising performance. Distributed word embeddings are utilized to represent semantic information. However, each word is associated with a single vector in the embedding layer
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A Data Efficient End-To-End Spoken Language Understanding Architecture
Many end-to-end architectures have been recently proposed for spoken language understanding (SLU) and semantic parsing. Based on a large amount of data, those models learn jointly acoustic and linguistic-sequential features. While those architectures give
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Efficient Algorithm To Implement Sliding Singular Spectrum Analysis With Application To Biomedical Signal Denoising
Previous work [1] has shown that Singular Spectrum Analysis (SSA) can be particularly effective at noise removal or signal separation in the case of single channel mixtures. The work presented here shows how the sliding or updating algorithm, which perfor
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Modeling Plate And Spring Reverberation Using A Dsp-Informed Deep Neural Network
Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Currently, they are often used in music production for aesthetic reasons due to their particular sonic characteristics.
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Channel Adversarial Training For Speaker Verification And Diarization
Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We propose a training strategy which aims to produce features that are invari
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Specaugment On Large Scale Datasets
Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this pape
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Crnn-Ctc Based Mandarin Keywords Spotting
Deep learning based approaches have greatly improved the performance of spoken keyword spotting (KWS). However, KWS of different languages should have their own corresponding modeling units to optimize the performance. In this paper, we propose an end-to-
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Cross-Domain Adaptation For Biometric Identification Using Photoplethysmogram
The adoption of biomedical signals such as photoplethysmogram (PPG) and electrocardiogram (ECG) for health parameter estimation on wearable devices is growing in tandem with the increase of attention in mobile healthcare. In our work, we use PPG signals e
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A Zeroth-Order Learning Algorithm For Ergodic Optimization Of Wireless Systems With No Models And No Gradients
Optimal resource allocation in real-world wireless systems is rather challenging, not only due to the unavailability of accurate statistical channel models, but also because expressions of maximal or achievable information rates are most often unknown, or
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Video Question Generation Via Semantic Rich Cross-Modal Self-Attention Networks Learning
We introduce a novel task, Video Question Generation (Video QG). A Video QG model automatically generates questions given a video clip and its corresponding dialogues. Video QG requires a range of skills -- sentence comprehension, temporal relation, the i
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Learning The Helix Topology Of Musical Pitch
To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively. This article addresses the problem of discovering this helical structure from
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On The Degrees Of Freedom In Total Variation Minimization
In the theory of linear model, the degrees of freedom (DOF) of an estimator plays a pivotal role in the risk estimation, as it quantifies the complexity of a statistical modeling procedure. Considering the total-variation (TV) regularization, we in this p
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Rethinking Temporal-Related Sample For Human Action Recognition
Temporal-related samples always have huge intra-class appearance variation, on which lots of existing action recognition algorithms have poor performance. In this paper, our motivation is to address this issue by utilizing temporal information more effect
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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