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Adaptive Matched Filter Using Non-Target Free Training Data
The problem of detecting a subspace signal in colored Gaussian noise with unknown covariance matrix is investigated when the training data may contain samples with target signal. The target signal is assumed that it lies in a subspace spanned by columns o
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Towards Real-Time, Multi-View Video Stereopsis
We present a real-time, multi-view video stereopsis (RTMVS) algorithm. This algorithm processes five synchronized video streams from cameras of a stationary camera array using a commodity laptop computer equipped with an Nvidia GPU. It provides 3D visuali
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Structured Citation Trend Prediction Using Graph Neural Networks
Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practiti
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Robust Rank Constrained Sparse Learning: A Graph-Based Method For Clustering
Graph-based clustering is an advanced clustering techniuqe, which partitions the data according to an affinity graph. However, the graph quality affects the clustering results to a large extent, and it is difficult to construct a graph with high quality,
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Parsing Map Guided Multi-Scale Attention Network For Face Hallucination
Face hallucination that aims to transform a low-resolution (LR) face image to a high-resolution (HR) one is an active domain-specific image super-resolution problem. The performance of existing methods is usually not satisfactory, especially when the upsc
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On Distributed Stochastic Gradient Descent For Nonconvex Functions In The Presence Of Byzantines
We consider the distributed stochastic optimization problem of minimizing a nonconvex function $f$ in an adversarial setting. All the $w$ worker nodes in the network are expected to send their stochastic gradient vectors to the fusion center (or server).
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Enhanced Non-Local Cascading Network With Attention Mechanism For Hyperspectral Image Denoising
Because of the complexity of imaging environment, hyperspectral remote sensing images (HSIs) often suffer from different kinds of noise. Despite the success in natural image denoising, most of the existing CNN-based HSIs denoising methods still suffer fro
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Slogd: Speaker Location Guided Deflation Approach To Speech Separation
Speech separation is the process of separating multiple speakers from an audio recording. In this work we propose to separate the sources using a Speaker LOcalization Guided Deflation (SLOGD) approach wherein we estimate the sources iteratively. In each i
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Semi-Supervised Sentence Classification Based On User Polarity In The Social Scenarios
The data sparsity is the main challenge in sentence classification in social scenarios, the recent methods incorporate user information by encoding user node in the user-relation network to alleviate this issue. However, the connection between users is no
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Interpretability-Guided Convolutional Neural Networks For Seismic Fault Segmentation
Delineating the seismic fault, which is an important type of geologic structures in seismic images, is a key step for seismic interpretation. Comparing with conventional methods that design a number of hand-crafted features based on the observed character
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Minimal Adversarial Perturbations In Mobile Health Applications: The Epileptic Brain Activity Case Study
Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing m
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A Fast And Accurate Frequent Directions Algorithm For Low Rank Approximation Via Block Krylov Iteration
It is known that frequent directions (FD) is a popular deterministic matrix sketching method for low rank approximation. However, FD and its randomized variants usually meet high computational cost or computational instability in dealing with large-scale
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Privacy-Preserving Pattern Recognition Using Encrypted Sparse Representations In L0 Norm Minimization
In this paper, we propose a privacy-preserving pattern recognition method that uses encrypted sparse representations in L0 norm minimization. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation perf
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Label Propagation Adaptive Resonance Theory For Semi-Supervised Continuous Learning
Semi-supervised learning and continuous learning are fundamental paradigms for human-level intelligence. To deal with real-world problems where labels are rarely given and the opportunity to access the same data is limited, it is necessary to apply these
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Efficient Super-Resolution Two-Dimensional Harmonic Retrieval Via Enhanced Low-Rank Structured Covariance Reconstruction
This paper develops an enhanced low-rank structured covariance reconstruction (LRSCR) method based on the decoupled atomic norm minimization (D-ANM), for super-resolution two-dimensional (2D) harmonic retrieval with multiple measurement vectors. This LRSC
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Deep Flow Collaborative Network For Online Visual Tracking
The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature ex
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On The Impact Of Language Familiarity In Talker Change Detection
The ability to detect talker changes when listening to conversational speech is fundamental to the perception and understanding of multi-talker speech. In this paper, we propose a novel experimental paradigm to provide insights on the impact of language f
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Addressing Accent Mismatch In Mandarin-English Code-Switching Speech Recognition
Automatic speech recognition systems suffer from accuracy degradation when code-switching (multiple languages are spoken in a single utterance) is encountered. This is especially common for non-native speakers where there is a mismatch between speech and
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Expression-Guided Eeg Representation Learning For Emotion Recognition
Learning a joint and coordinated representation between different modalities can improve multimodal emotion recognition. In this paper, we propose a deep representation learning approach for emotion recognition from electroencephalogram (EEG) signals guid
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Improving Auditory Attention Decoding Performance Of Linear And Non-Linear Methods Using State-Space Model
Identifying the target speaker in hearing aid applications is crucial to improve speech understanding. Recent advances in electroencephalography (EEG) have shown that it is possible to identify the target speaker from single-trial EEG recordings using aud
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Meta-Learning To Communicate: Fast End-To-End Training For Fading Channels
When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach
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Lipreading Using Temporal Convolutional Networks
Lip-reading has attracted a lot of research attention lately thanks to advances in deep learning. The current state-of-the-art model for recognition of isolated words in-the-wild consists of a residual network and Bidirectional Gated Recurrent Unit (BGRU)
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Improving Cross-Dataset Performance Of Face Presentation Attack Detection Systems Using Face Recognition Datasets
Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under
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Orthogonal Training For Text-Independent Speaker Verification
In this paper we propose orthogonal training schemes to improve the effectiveness of cosine similarity measurements in text-independent speaker verification (SV) tasks. Compared to the PLDA backend, cosine similarity is simple to compute, and it does not
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Multi-Layer Content Interaction Through Quaternion Product For Visual Question Answering
Multi-modality fusion technologies have greatly improved the performance of neural network-based Video Description/Caption, Visual Question Answering (VQA) and Audio Visual Scene-aware Dialog (AVSD) over the recent years. Most previous approaches only exp
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On The Choice Of Graph Neural Network Architectures
Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a sig
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Griffin–Lim Like Phase Recovery Via Alternating Direction Method Of Multipliers
Recovering a signal from its amplitude spectrogram, or phase recovery, exhibits many applications in acoustic signal processing. When only an amplitude spectrogram is available and no explicit information is given for the phases, the Griffin-Lim algorithm
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Bilevel Optimization Using Stationary Point Of Lower-Level Objective Function
In this letter, we address an audio signal separation problem and propose a new effective algorithm for solving a bilevel optimization in discriminative nonnegative matrix factorization (NMF). Recently, discriminative training of NMF bases has been develo
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Deepjscc: The Future Of Wireless Video Transmission
We propose a demonstration of a joint source-channel coding (JSCC) scheme, called DeepJSCC, for wireless video transmission. Unlike conventional digital communication systems, which rely on separate source and channel coding, DeepJSCC is a purely data-dri
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A Random Gossip Bmuf Process For Neural Language Modeling
Neural network language model (NNLM) is an essential component of industrial ASR systems. One important challenge of training an NNLM is to leverage between scaling the learning process and handling big data. Conventional approaches such as block momentum
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Consistency-Aware Multi-Channel Speech Enhancement Using Deep Neural Networks
This paper proposes a deep neural network (DNN)--based multi-channel speech enhancement system in which a DNN is trained to maximize the quality of the enhanced time-domain signal. DNN-based multi-channel speech enhancement is often conducted in the time-
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Unsupervised Training For Deep Speech Source Separation With Kullback-Leibler Divergence Based Probabilistic Loss Function
In this paper, we propose a multi-channel speech source separation method with a deep neural network (DNN) which is trained under the condition that no clean signal is available. As an alternative to a clean signal, the proposed method adopts an estimated
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A Deep Gradient Boosting Network For Optic Disc And Cup Segmentation
Segmentation of optic disc (OD) and optic cup (OC) is critical in automated fundus image analysis system. Existing state-ofthe-arts focus on designing deep neural networks with one or multiple dense prediction branches. Such kind of designs ignore connect
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Feature Selection Under Orthogonal Regression With Redundancy Minimizing
Various supervised embedded methods have been proposed to select discriminative features from original ones, such as Feature Selection with Orthogonal Regression (FSOR) and Robust Feature Selection. Compared with embedded methods based on the least square
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Audio Codec Enhancement With Generative Adversarial Networks
Audio codecs are typically transform-domain based and efficiently code stationary audio signals, but they struggle with speech and signals containing dense transient events such as applause. Specifically, with these two classes of signals as examples, we
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Differentiable Branching In Deep Networks For Fast Inference
In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them con
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Cross Image Cubic Interpolator For Spatially Varying Exposures
Spatially varying exposures via rolling shutter is an efficient way to capture differently exposed images for high dynamic range (HDR) scenes. Neither camera movement nor moving objects is an issue for such a captured method. However, a possible issue is
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Beyond The Dcase 2017 Challenge On Rare Sound Event Detection: A Proposal For A More Realistic Training And Test Framework
There are many ways to evaluate rare sound event detection (SED) approaches, e.g., the DCASE 2017 challenge provides a widely employed framework. This paper proposes a rare SED training and test framework, which is reflecting an SED application in a more
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Lie Group State Estimation Via Optimal Transport
Many applications in science and engineering involve tracking the state of a stochastic differential equation (SDE) evolving in a Lie group. This has been tackled by particle filtering although some existing schemes fail to satisfy geometric constraints.
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Distributed Quantization For Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discrete bit-constrained representation. Quantization is typically carried out using analog-to-digital converters (ADCs), operating in a serial scalar manner. In some applications, a set of
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Maximum Likelihood Estimation Of The Interference-Plus-Noise Cross Power Spectral Density Matrix For Own Voice Retrieval
In headset and hearing aid applications, it is of interest to retrieve the user's own voice in a noisy environment, e.g. for telephony applications. To do so, the cross power spectral density (CPSD) of the noise is required. In this paper, a novel maximum
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Multitask Learning And Multistage Fusion For Dimensional Audiovisual Emotion Recognition
Due to its ability to accurately predict emotional state using multimodal features, audiovisual emotion recognition has recently gained more interest from researchers. This paper proposes two methods to predict emotional attributes from audio and visual d
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Joint Resource Allocation And Routing For Service Function Chaining With In-Subnetwork Processing
Network Function Virtualization (NFV) is an efficient approach to simplify and accelerate the deployment of diverse network services. A critical challenge lies in mapping Virtual Network Functions (VNFs) to high-volume servers, resource allocation, and tr
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On-The-Fly Feature Selection And Classification With Application To Civic Engagement Platforms
Online feature selection and classification is crucial for time sensitive decision making. Existing work however either assumes that features are independent or produces a fixed number of features for classification. Instead, we propose an optimal framewo
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Mutual-Information-Based Sensor Placement For Spatial Sound Field Recording
A sensor (microphone) placement method based on mutual information for spatial sound field recording is proposed. The sound field recording methods using distributed sensors enable the estimation of the sound field inside a target region of arbitrary shap
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Dynamic Variational Autoencoders For Visual Process Modeling
This work studies the problem of modeling visual processes by leveraging deep generative architectures for learning linear, Gaussian representations from observed sequences. We propose a joint learning framework, combining a vector autoregressive model an
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Exploiting Rays In Blind Localization Of Distributed Sensor Arrays
Many signal processing algorithms for distributed sensors are capable of improving their performance if the positions of sensors are known. In this paper, we focus on estimators for inferring the relative geometry of distributed arrays and sources, i.e. t