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Cell-Phone Classification: A Convolutional Neural Network Approach Exploiting Electromagnetic Emanations
In this paper, we propose a methodology to identify both the brand of a cell-phone, and the status of its camera by exploiting electromagnetic (EM) emanations. The method composes two parts: Feature extraction and Convolutional Neural Netwotk (CNN). We fi
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Supervised Encoding For Discrete Representation Learning
Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels. Such a learning framework typically lacks the intuition that encoded features from the same class tend
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Binary Probability Model For Learning Based Image Compression
In this paper, we propose to enhance learned image compression systems with a richer probability model for the latent variables. Previous works model the latents with a Gaussian or a Laplace distribution. Inspired by binary arithmetic coding, we propose t
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Fast Single-View 3D Object Reconstruction With Fine Details Through Dilated Downsample And Multi-Path Upsample Deep Neural Network
Three-dimensional (3D) object reconstruction is among the mostimportant research areas in the field of computer vision. Its pur-pose is to reconstruct the overall shape of an object from its two-dimensional (2D) image. With the development of deep learnin
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Cad-Aec: Context-Aware Deep Acoustic Echo Cancellation
Deep-learning based acoustic echo cancellation (AEC) methods have been shown to outperform the classical techniques. The main drawback of the learning-based AEC is its dependency on the training set, which limits its practical deployment in mobile devices
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Gfnet: A Lightweight Group Frame Network For Efficient Human Action Recognition
Human action recognition aims at assigning an action label to a well-segmented video. Recent work using two-stream or 3D convolutional neural networks achieves high recognition rates at the cost of huge computation complexity, memory footprint, and parame
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Multi-View Wasserstein Discriminant Analysis With Entropic Regularized Wasserstein Distance
Multi-view data analysis has recently garnered increasing attention because multi-view data frequently appear in real-world applications, which are collected or taken from many sources or captured using various sensors. A simple and popular promising appr
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Dispersive Grid-Free Orthogonal Matching Pursuit For Modal Estimation In Ocean Acoustics
Considering low-frequency acoustic sources, shallow-water environments act as modal dispersive waveguides. In this context, the signal can be described as a sum of a few modal components, each of them propagating with its own wavenumber. When dealing with
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Local Key Estimation In Classical Music Recordings: A Cross-Version Study On Schubert’S Winterreise
While global key and chord estimation for both popular and classical music recordings have received a lot of attention, little research has been devoted to estimating the local key for classical music. In this work, we approach local key estimation on a u
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Fourier Phase Retrieval With Arbitrary Reference Signal
Fourier phase retrieval problem aims at recovering a signal from its Fourier amplitude measurements. A good initialization and prior information about the sparsity or support of the target signal is critical for robust recovery. Holographic phase retrieva
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Robust Marine Buoy Placement For Ship Detection Using Dropout K-Means
Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy
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Time-Domain Audio Source Separation Based On Wave-U-Net Combined With Discrete Wavelet Transform
We propose a time-domain audio source separation method using down-sampling (DS) and up-sampling (US) layers based on a discrete wavelet transform (DWT). The proposed method is based on one of the state-of-the-art deep neural networks, Wave-U-Net, which s
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Spidernet: Attention Network For One-Shot Anomaly Detection In Sounds
We propose a similarity function for one-shot anomaly detection in sounds (ADS) called SPecific anomaly IDentifiER network (SPIDERnet). In ADS systems, since overlooking an anomaly may result in serious incidents, we need to update such systems using an (
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A Forward-Backward Algorithm For Reweighted Procedures: Application To Radio-Astronomical Imaging
During the last decades, reweighted procedures have shown high efficiency in computational imaging. They aim to handle non-convex composite penalization functions by iteratively solving multiple approximated sub-problems. Although the asymptotic behaviour
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Interpolation And Range Extrapolation Of Sound Source Directivity Based On A Spherical Wave Propagation Model
Approaches for incorporating sound source directivity into wave-based room acoustic simulations using a spherical harmonic representation have been presented recently. Normally, the directivity is measured or prescribed on a spherical surface centered at
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Revealing Backdoors, Post-Training, In Dnn Classifiers Via Novel Inference On Optimized Perturbations Inducing Group Misclassification
Recently, a special type of data poisoning (DP) attack against deep neural network (DNN) classifiers, known as a backdoor, was proposed. These attacks do not seek to degrade classification accuracy, but rather to have the classifier learn to classify to a
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Let-Sne: A Hybrid Approach To Data Embedding And Visualization Of Hyperspectral Imagery
Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational t
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Multi-Step Online Unsupervised Domain Adaptation
In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem, where the target data are unlabelled and arriving sequentially. The traditional methods on the OUDA problem mainly focus on transforming each arriving target data to the s
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Language-Agnostic Multilingual Modeling
Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of data-rich and data-scarce languages in a single model. This enables data and parameter sharing across languages, which is especially beneficial for the data-scarce lan
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Extended Cyclic Coordinate Descent For Robust Row-Sparse Signal Reconstruction In The Presence Of Outliers
The problem of row-sparse signal reconstruction for complex-valued data with outliers is investigated in this paper. First, we formulate the problem by taking advantage of a sparse weight matrix, which is used to down-weight the outliers. The formulated p
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Composite Dynamic Texture Synthesis Using Hierarchical Linear Dynamical System
We demonstrate that a systematic inclusion of prior structural constraints on the states of a linear dynamical system significantly improves its ability to model complex multidimensional sequences. This constrained LDS, typically termed as the hierarchica
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Robust Hybrid Beamforming For Satellite-Terrestrial Integrated Networks
In this paper, we propose a novel robust downlink beamforming (BF) design for satellite-terrestrial integrated networks. Under a realistic assumption that the angular information of eavesdroppers is not perfectly known, we establish an optimization framew
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Environment-Aware Reconfigurable Noise Suppression
The paper proposes an efficient, robust, and reconfigurable technique to suppress various types of noises for any sampling rate. The theoretical analyses, subjective and objective test results show that the proposed noise suppression (NS) solution signifi
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A Lightweight Multi-Label Segmentation Network For Mobile Iris Biometrics
This paper proposes a novel, lightweight deep convolutional neural network specifically designed for iris segmentation of noisy images acquired by mobile devices. Unlike previous studies, which only focused on improving the accuracy of segmentation mask u
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A Penalty Alternating Direction Method Of Multipliers For Decentralized Composite Optimization
This paper proposes a penalty alternating direction method of multipliers (ADMM) to minimize the summation of convex composite functions over a decentralized network. Each agent in the network holds a private function consisting of a smooth part and a non
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Pan: Phoneme-Aware Network For Monaural Speech Enhancement
Current methods for monaural speech enhancement only utilize acoustic information but ignore the phonetic information of an utterance. In the voice conversion community, significant progress has been achieved by using the phonetic information via the phon
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Active Noise Control Over Multiple Regions: Performance Analysis
Active noise control (ANC) over space is a well-researched topic where multi-microphone, multi-loudspeaker systems are designed to minimize the noise over a spatial region of interest. In this paper, we perform an initial study on the more complex problem
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Adversarial Text Image Super-Resolution Using Sinkhorn Distance
Convolutional neural network-based methods have demonstrated promising results for single image super-resolution. However, existing methods usually approach the problem on natural scenes rather than texts, whereas the latter can provide more informative m
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Partial Auc Optimization Based Deep Speaker Embeddings With Class-Center Learning For Text-Independent Speaker Verification
Deep embedding based text-independent speaker verification has demonstrated superior performance to traditional methods in many challenging scenarios. Its loss functions can be generally categorized into two classes, i.e., verification and identification.
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Evaluation Of Deep-Learning-Based Voice Activity Detectors And Room Impulse Response Models In Reverberant Environments
State-of-the-art deep-learning-based voice activity detectors (VADs) are often trained with anechoic data. However, real acoustic environments are generally reverberant, which causes the performance to significantly deteriorate. To mitigate this mismatch
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Decentralized Optimization With Non-Identical Sampling In Presence Of Stragglers
We consider decentralized consensus optimization when workers sample data from non-identical distributions and perform variable amounts of work due to slow nodes known as stragglers. The problem of non-identical distributions and the problem of variable a
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Energy Disaggregation From Low Sampling Frequency Measurements Using Multi-Layer Zero Crossing Rate
Non-Intrusive Load Monitoring aims to disaggregate the energy consumption measurements of a smart-meter from household to device level. Most commercial smart-meters measure at 1 Hz or slightly higher, in order to be able to wirelessly transmit the acquire
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Receptive Field Pyramid Network For Object Detection
Current state-of-the-art methods usually utilize feature pyramid to provide various receptive fields for detecting objects at different scales. However, the feature maps from low- to high-level layers have large semantic gaps and are with different spatia
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But System For The Second Dihard Speech Diarization Challenge
This paper describes the winning systems developed by the BUT team for the four tracks of the second DIHARD speech diarization challenge. For tracks 1 and 2 the systems were mainly based on performing agglomerative hierarchical clustering (AHC) of x-vecto
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A Frequency-Domain Bss Method Based On L1 Norm, Unitary Constraint, And Cayley Transform
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We propose a frequency-domain blind source separation method that uses (a) the L1 norm of orthonormal vectors of estimated source signals as a sparsity measure and (b) Cayley transform for optimizing the objective function under the unitary constraint in
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Sparse Beamspace Equalization For Massive Mu-Mimo Mmwave Systems
We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on
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Non-Griffin–Lim Type Signal Recovery From Magnitude Spectrogram
Speech and audio signal processing frequently requires to recover a time-domain signal from the magnitude of a spectrogram. Conventional methods inversely transform the magnitude spectrogram with a phase spectrogram recovered by the Griffin?Lim algorithm
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A Fast Proximal Point Algorithm For Generalized Graph Laplacian Learning
Graph learning is one of the most important tasks in machine learning, statistics and signal processing. In this paper, we focus on the problem of learning the generalized graph Laplacian (GGL) and propose an efficient algorithm to solve it. We first full
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Exploring Bio-Behavioral Signal Trajectories Of State Anxiety During Public Speaking
Public speaking anxiety (PSA) is among the top social phobias in the world. Quantifying PSA in a reliable and unobtrusive manner can lay the foundation toward personalized and inexpensive technology-based interventions. Existing work for quantifying PSA o
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Short And Squeezed: Accelerating The Computation Of Antisparse Representations With Safe Squeezing
Antisparse coding aims at spreading the information uniformly over representation coefficients and can be expressed as the solution of an $ell_infty$-norm regularized problem. In this paper, we propose a new methodology, coined ``safe squeezing'', accel
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Multigraph Spectral Clustering For Joint Content Delivery And Scheduling In Beam-Free Satellite Communications
This paper tackles the problem of user scheduling in satellite content delivery networks with precoding. The clustering process has to consider two crucial and independent characteristics of the user terminals. On the one hand, users belonging to the same
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Fine-Grained Action Recognition On A Novel Basketball Dataset
Currently most works on action recognition focus on the coarsely-grained actions, while the fine-grained action recognition is seldom addressed which is of vital importance in many applications such as video retrieval. To tackle this issue, in this paper,
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Progressive Multi-Target Network Based Speech Enhancement With Snr-Preselection For Robust Speaker Diarization
In this paper, we design a novel front-end processing system for speaker diarization under realistic conditions with challenging background noises. To cope with diversified environments, we first extend our previously proposed progressive learning based s
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Social Learning With Partial Information Sharing
This work studies the learning abilities of agents sharing partial beliefs over social networks. The agents observe data that could have risen from one of several hypotheses and interact locally to decide whether the observations they are receiving have r
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Learning To Fool The Speaker Recognition
Due to the widespread deployment of fingerprint/face/speaker recognition systems, attacking deep learning based biometric systems has drawn more and more attention. Previous research mainly studied the attack to the vision-based system, such as fingerprin
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Low-Bit Quantization Of Recurrent Neural Network Language Models Using Alternating Direction Methods Of Multipliers
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing e
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Facial Feature Embedded Cyclegan For Vis-Nir Translation
Visible and near-infrared (VIS-NIR) face recognition remains a challenging task due to distinctions between spectral components of two modalities. Inspired by the CycleGAN, this paper presents a method aiming to translate between VIS and NIR face images.
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Detection Of Mild Dyspnea From Pairs Of Speech Recordings
Shortness of breath, or dyspnea is a condition of the cardio-pulmonary system which may be caused by, for example, a heart or lung disease, or physical load. In this paper, we explore techniques of detecting mild dyspnea directly from conversational speec
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Efficient Estimation Of Mixing Matrix Using A Two-Sensor Array
Blind source separation involves the estimation of mixing matrix given only observed mixtures. Considering the computational cost in practice, this paper proposes an efficient mixing matrix estimation (MME) method using an easily configured two-sensor arr
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Independent Language Modeling Architecture For End-To-End Asr
The attention-based end-to-end (E2E) automatic speech recognition (ASR) architecture allows for joint optimization of acoustic and language models within a single network. However, in a vanilla E2E ASR architecture, the decoder sub-network (subnet), which