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Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus In Wireless Sensor Networks
In many applications of wireless sensor networks, it is important that the privacy of the nodes of the network be protected. Therefore, privacy-preserving algorithms have received quite some attention recently. In this paper, we propose a novel convex opt
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Phase Reconstruction Based On Recurrent Phase Unwrapping With Deep Neural Networks
Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio synthesis. To take advantage of rich knowledge from data, several studies pr
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Optimized Sensor Selection For Joint Radar-Communication Systems
Sensor array-based joint radar-communication (JRC) systems exploit adaptive beamforming to transmit radar and communication signals in their respective directions. Optimal sensor selection is anticipated as an attractive means to achieve superior performa
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Privacy-Aware Quickest Change Detection
This paper considers the problem of the quickest detection of a change in distribution while taking privacy considerations into account. Our goal is to sanitize the signal to satisfy information privacy requirements while being able to detect a change qui
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Speech Breathing Estimation Using Deep Learning Methods
Breathing is the primary mechanism for maintaining the sub-glottal pressure for speech production. Speech can be seen as a systematic outflow of air during exhalation characterized by linguistic content and prosodic factors. Thus, sensing respiratory dyna
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Low Complexity Single Image Super-Resolution With Channel Splitting And Fusion Network
Recently, deep convolutional neural networks (CNNs) have made remarkable progress on single image super-resolution (SISR). However, many of these methods use very deep or wide convolutional layers to achieve good performance, which treat all feature chann
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Auto-Fas: Searching Lightweight Networks For Face Anti-Spoofing
With the development of mobile devices, it is hopeful and pressing to deploy face recognition and face anti-spoofing (FAS) model on cell phone or portable devices. Most of existing face anti-spoofing methods focus on building computational costly detector
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A Low-Latency Successive Cancellation Hybrid Decoder For Convolutional Polar Codes
By adopting successive cancellation list decoding (SCL), polar codes demonstrate competitive error correction performance over LDPC and Turbo codes. However, SCL decoding suffers from high computational complexity and long decoding latency, especially whe
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Speech Sentiment Analysis Via Pre-Trained Features From End-To-End Asr Models
In this paper, we propose to use pre-trained features from end-to-end ASR models to solve speech sentiment analysis as a down-stream task. We show that end-to-end ASR features, which integrate both acoustic and text information from speech, achieve promis
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Intelligent Reflecting Surface For Massive Device Connectivity: Joint Activity Detection And Channel Estimation
Intelligent Reflecting Surface (IRS) has been a promising solution to enhance wireless networks both spectral-efficiently and energy-efficiently. This paper considers an IRS-assisted the Internet of Things network for massive connectivity. We aim to solve
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Learning To Characterize Adversarial Subspaces
Deep Neural Networks (DNNs) are known to be vulnerable to the maliciously generated adversarial examples. To detect these adversarial examples, previous methods use artificially designed metrics to characterize the properties of adversarial subspaces wher
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Fast And Accurate Embedded Dcnn For Rgb-D Based Sign Language Recognition
In this paper, fast and accurate two paths CNN architecture was designed in hardware-oriented manner. Our proposed network is composed of RGB and depth path for gesture recognition by fusing RGB and depth features, following the pre-defined constraints on
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A Multitaper Reassigned Spectrogram For Increased Time-Frequency Localization Precision
The reassignment vectors of the matched reassigned spectrogram (MRS) have shown to be sensitive to noise, with resulting degraded precision in the time-frequency localization. In this paper we propose a multitaper reassignment (mtRS) method for estimation
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Levenberg-Marquardt And Line-Search Extended Kalman Smoothers
The aim of this article is to present Levenberg-Marquardt and line-search extensions of the classical iterated extended Kalman smoother (IEKS) which has previously been shown to be equivalent to the Gauss-Newton method. The algorithms are derived by rewri
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A Maximum Likelihood Approach To Multi-Objective Learning Using Generalized Gaussian Distributions For Dnn-Based Speech Enhancement
The multi-objective learning using minimum mean squared error criterion for DNN-based speech enhancement (MMSE-MOL-DNN) has been demonstrated to achieve better performance than single output DNN. However, one problem of MMSE-MOL-DNN is that the prediction
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Self-Supervised Adversarial Training
Recent work has demonstrated that neural networks are vulnerable to adversarial examples. To escape from the predicament, many works try to harden the model in various ways, in which adversarial training is an effective way which learns robust feature rep
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An Optimal Channel Estimation Scheme For Intelligent Reflecting Surfaces Based On A Minimum Variance Unbiased Estimator
In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. We show th
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A Dynamic Stream Weight Backprop Kalman Filter For Audiovisual Speaker Tracking
Audiovisual speaker tracking is an application that has been tackled by a wide range of classical approaches based on Gaussian filters, most notably the well-known Kalman filter. Recently, a specific Kalman filter implementation was proposed for this task
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Detecting Adversarial Attacks In Time-Series Data
In recent times, deep neural networks have seen increased adoption in highly critical tasks. They are also susceptible to adversarial attacks, which are specifically crafted changes made to input samples which lead to erroneous output from such models. Su
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Latency-Minimized Design Of Secure Transmissions In Uav-Aided Communications
Unmanned aerial vehicles (UAVs) can be utilized as aerial base stations to provide communication service for remote mobile users due to their high mobility and flexible deployment. However, the line-of-sight (LoS) wireless links are vulnerable to be inter
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Focus On Semantic Consistency For Cross-Domain Crowd Understanding
For pixel-level crowd understanding?it is time-consuming and laborious in data collection and annotation. Some domain adaptation algorithms try to liberate it by training models with synthetic data, and the results in some recent works have proved the fea
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Bringing In The Outliers: A Sparse Subspace Clustering Approach To Learn A Dictionary Of Mouse Ultrasonic Vocalizations
Mice vocalize in the ultrasonic range during social interactions. These vocalizations are used in neuroscience and clinical studies to tap into complex behaviors and states. The analysis of these ultrasonic vocalizations (USVs) has been traditionally a ma
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No-Regret Non-Convex Online Meta-Learning
The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past tasks for fast learning of future tasks, and online-learning which tackles th
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A Semi-Supervised Approach For Identifying Abnormal Heart Sounds Using Variational Autoencoder
Abnormal heart sounds may have diverse frequency characteristics depending upon underlying pathological conditions. Designing a binary classifier for predicting normal and abnormal heart sounds using supervised learning requires a lot of training data, co
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Zero-Shot Multi-Speaker Text-To-Speech With State-Of-The-Art Neural Speaker Embeddings
While speaker adaptation for end-to-end speech synthesis using speaker embeddings can produce good speaker similarity for speakers seen during training, there remains a gap for zero-shot adaptation to unseen speakers. We investigate multi-speaker modeling
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Srzoo: An Integrated Repository For Super-Resolution Using Deep Learning
Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However, their implementations and evaluations are dispersed in terms of various dee
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Detection And Analysis Of T/D Deletion In Librispeech
In this study we developed a new method for automatic identification of t/d deletion. Our method achieved 94% accuracy on TIMIT and 87% on human-annotated data from Librispeech. We then conducted an analysis of t/d deletion on more than half of a million
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Multi-Task Learning For Speaker Verification And Voice Trigger Detection
Automatic speech transcription and speaker recognition are usually treated as separate tasks even though they are interdependent. In this study, we investigate training a single network to perform both tasks jointly. We train the network in a supervised m
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Alternative Half-Sample Interpolation Filters For Versatile Video Coding
To reduce the residual energy of a video signal, motion compensated prediction with fractional-sample accuracy has been successfully employed in modern video coding technology. In contrast to the fixed quarter-sample motion vector resolution for the luma
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Reversal No Longer Matters: Attention-Based Arrhythmia Detection With Lead-Reversal Ecg Data
In this paper, we propose an attention-based multi-scale neural network for arrhythmia detection with lead-reversal electrocardiogram data. Electrocardiogram with a set of 12 waveforms(known as 12-lead ECG) measures myocardial electrophysiological activit
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Message Transmission Through Underspread Time-Varying Linear Channels
It is common to model rapidly varying communication channels by time-varying linear systems. The output of a time-varying linear system can be described by a superposition of time-frequency (delay-Doppler) shifts of the input signal. This paper investigat
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The Compressed Nested Array For Underdetermined Doa Estimation By Fourth-Order Difference Coarray
In this paper, a new sparse array structure, which further improves the degrees of freedom (DOFs) and enhanced the DOA estimation performance, for the fourth-order cumulant based direction of arrival (DOA) estimation is proposed. The new-formed array is h
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Improved Large-Margin Softmax Loss For Speaker Diarisation
Speaker diarisation systems nowadays use embeddings generated from speech segments in a bottleneck layer, which are needed to be discriminative for unseen speakers. It is well-known that large-margin training can improve the generalisation ability to unse
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Conditional Mutual Information Neural Estimator
Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rate based on aggregated samples from
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Propeller Noise Detection With Deep Learning
Due to the complexity of environment and source modelling, underwater target detection is a rather challenging task. In the Deep Learning community, many attempts were made to deal with this problem, mainly through expert features, but few assessed the be
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Approximate Bayesian Computation With The Sliced-Wasserstein Distance
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulated dat
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Learning Signed Graphs From Data
Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and fl
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Sampling Strategies For Gan Synthetic Data
Generative Adversarial Networks (GANs) have been used widely to generate large volumes of synthetic data. This data is being utilized for augmenting with real examples in order to train deep Convolutional Neural Networks (CNNs). Studies have shown that th
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Automatic Data Augmentation Via Deep Reinforcement Learning For Effective Kidney Tumor Segmentation
Conventional data augmentation realized by performing simple pre-processing operations (e.g., rotation, crop, etc.) has been validated for its advantage in enhancing the performance for medical image segmentation. However, the data generated by these conv
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Robust Phase Retrieval With Outliers
An outlier-resistance phase retrieval algorithm based on alternating direction method of multipliers (ADMM) is devised in this paper. Instead of the widely used least squares criterion that is only optimal for Gaussian noise environment, we adopt the leas
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Relative Cost Based Model Selection For Sparse High-Dimensional Linear Regression Models
In this paper, we propose a novel model selection method named multi-beta-test (MBT) for the sparse high-dimensional linear regression model. The estimation of the correct subset in the linear regression problem is formulated as a series of hypothesis tes
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A Neural Network-Based Spike Sorting Feature Map That Resolves Spike Overlap In The Feature Space
When inserting an electrode array in the brain, its electrodes will record so-called 'spikes' which are generated by the neurons in the neighbourhood of the array. Spike sorting is the process of detecting and assigning these recorded spikes to their puta
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Accurate And Scalable Version Identification Using Musically-Motivated Embeddings
The version identification (VI) task deals with the automatic detection of recordings that correspond to the same underlying musical piece. Despite many efforts, VI is still an open problem, with much room for improvement, specially with regard to combini
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Blind Inference Of Centrality Rankings From Graph Signals
We study the blind centrality ranking problem, where our goal is to infer the eigenvector centrality ranking of nodes solely from nodal observations, i.e., without information about the topology of the network. We formalize these nodal observations as gra
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Unsupervised Image-To-Image Translation Via Fair Representation Of Gender Bias
Fairness becomes a critical issue of computer vision to reduce discriminative factors in various systems. Among computer vision tasks, Image-to-Image translation for facial attributes editing can yield discriminative results. The unexpected gender changed
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Better Safe Than Sorry: Risk-Aware Nonlinear Bayesian Estimation
Despite the simplicity and intuitive interpretation of minimum mean squared error (MMSE) estimators, their effectiveness in certain scenarios is questionable. Indeed, minimizing squared errors on average does not provide any form of stability, as the vola
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Mixture Factorized Auto-Encoder For Unsupervised Hierarchical Deep Factorization Of Speech Signal
Speech signal is constituted and contributed by various informative factors, such as linguistic content and speaker characteristic. There have been notable recent studies attempting to factorize speech signal into these individual factors without requirin
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Active Semi-Supervised Learning For Diffusions On Graphs
Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated ra
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Towards An Efficient And General Framework Of Robust Training For Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several fundamental inference tasks. As a result, there is a surge of interest in using these models for making potentially important decisions in high-regret applications. However, despite GN