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Resilient To Byzantine Attacks Finite-Sum Optimization Over Networks
This contribution deals with distributed finite-sum optimization for learning over networks in the presence of malicious Byzantine attacks. To cope with such attacks, resilient approaches so far combine stochastic gradient descent (SGD) with different rob
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A Comparative Study Of Western And Chinese Classical Music Based On Soundscape Models
Whether literally or suggestively, the concept of soundscape is alluded in both modern and ancient music. In this study, we examine whether we can analyze and compare Western and Chinese classical music based on soundscape models. We addressed this questi
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Pitch Estimation Via Self-Supervision
We present a method to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. In contrast to existing methods, our neural network can be fully trained only on unlabeled data, using self-supervision. A tiny amount of
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Load Management With Predictions Of Solar Energy Production For Cloud Data Centers
Power supply of big infrastructures is today a tremendous operational cost for providers and the expected growth of Internet traffic and services will lead to a further expansion of the computing and networking infrastructures and this, in its turn, raise
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Enhanced Method Of Audio Coding Using Cnn-Based Spectral Recovery With Adaptive Structure
A process of spectral recovery can enhance the performance of transform-based audio coding by transmitting only a portion of spectral data and recovering the missing spectral data in the decoder. This study proposes an enhanced method of audio coding base
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Urtis: A Small 3D Imaging Sonar Sensor For Robotic Applications
State-of-the-art autonomous vehicles mainly rely on optical sensors to perceive their environment. However, the performance of these sensors worsens dramatically in environments where airborne particles are present. Sonar sensors rely on acoustic waves wh
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Near Capacity Rcqd Constellations For Papr Reduction Of Ofdm Systems
We investigate an optimized blind SeLected Mapping (SLM) algorithm to reduce the Peak-to-Average Power Ratio (PAPR) for Orthogonal Frequency Division Multiplexing (OFDM) systems with Signal Space Diversity (SSD). Several phase sequences based on two Rotat
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Multiple Sclerosis (MS) lesion segmentation from MR images is important for neuroimaging analysis. MS is diffuse, multifocal, and tend to involve peripheral brain structures such as the white matter, corpus callosum, and brainstem. Recently, U-Net has mad
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End-To-End Speech Translation With Self-Contained Vocabulary Manipulation
In machine translation, vocabulary manipulation is a way to reduce the target vocabulary based on the source sentence and the word dictionary, which is effective to lower latency during inference for text translation in industrial application. But vocabul
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Effect Of Choice Of Probability Distribution, Randomness, And Search Methods For Alignment Modeling In Sequence-To-Sequence Text-To-Speech Synthesis Using Hard Alignment
Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. Th
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Ecg Heartbeat Classification Based On Multi-Scale Wavelet Convolutional Neural Networks
This paper proposes a novel Deep Learning technique for ECG beats classification. Unlike the traditional Deep Learning models, a new Multi-Scale Wavelet Convolutional Neural Networks (MS-WCNN) is proposed to recognize automatically various cardiac arrhyth
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Convergence-Guaranteed Independent Positive Semidefinite Tensor Analysis Based On Student's T Distribution
In this paper, we address a blind source separation (BSS) problem and propose a new extended framework of independent positive semidefinite tensor analysis (IPSDTA). IPSDTA is a state-of-the-art BSS method that enables us to take interfrequency correlatio
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Nearest Kronecker Product Decomposition Based Normalized Least Mean Square Algorithm
Recently, nearest Kronecker product (NKP) decomposition based Wiener filter and Recursive Least Squares (RLS) have been proposed and was found to be a good candidate for system identification and echo cancellation and was shown to offer better tracking pe
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Accounting For Microprosody In Modeling Intonation
Intonation models are often used for the generation of fundamental frequency (f0) contours in speech synthesis. Current intonation models only represent the intentional f0 components that are related to the phonological structure of the utterance. However
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Time-Predictable Software-Defined Architecture With Sdf-Based Compiler Flow For 5G Baseband Processing
The advent of 5G networks motivates the need for high-performance, low-power, time-predictable hardware that can handle the aggressive real-time latency and throughput requirements of baseband processing. With newer generations like 5G, programmable hardw
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Data Selection Kernel Conjugate Gradient Algorithm
In recent years, the interest in kernel methods has increased exponentially, mainly due to applications including phenomena that cannot be well modeled by linear systems. Furthermore, the demand for high-speed communications and improvement in computer ca
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Learning Spatio-Temporal Convolutional Network For Real-Time Object Tracking
Siamese series of tracking networks have shown great potentials in achieving balanced accuracy and beyond real-time speed. However, most of existing siamese trackers only consider appearance features of first frame, and hardly benefit from interframe info
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Improving Language Identification For Multilingual Speakers
Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, how
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A Comprehensive Study Of Residual Cnns For Acoustic Modeling In Asr
Long short-term memory (LSTM) networks are the dominant architecture for large vocabulary continuous speech recognition (LVCSR) acoustic modeling due to their good performance. However, LSTMs are hard to tune and computationally expensive. To build a syst
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Effective Approximation Of Bandlimited Signals And Their Samples
Shannon's sampling theorem is of high importance in signal processing, because it links the continuous-time and discrete-time worlds. For bandlimited signals we can switch from one domain into the other without loosing information. In this paper we analyz
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Enhanced Mixture Population Monte Carlo Via Stochastic Optimization And Markov Chain Monte Carlo Sampling
The population Monte Carlo (PMC) algorithm is a popular adaptive importance sampling (AIS) method used for approximate computation of intractable integrals. Over the years, many advances have been made in the theory and implementation of PMC schemes. The
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A Monte Carlo Search-Based Triplet Sampling Method For Learning Disentangled Representation Of Impulsive Noise On Steering Gear
The classification task of impact noise on vehicle steering system mainly addresses the issue of modeling the transient and impulsive nature. Though various deep learning models including triplet network have been developed, the existing triplet network b
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An Optimal Symmetric Threshold Strategy For Remote Estimation Over The Collision Channel
A wireless sensing system with n sensors, observing independent and identically distributed continuous random variables with a symmetric probability density function, and one non-collocated estimator acting as a fusion center is considered. The sensors tr
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Depth Map Fingerprinting And Splicing Detection
With the ubiquity of social networks, images have become crucial in todays exchange of information. Most of these images are taken by smartphones. For forensic approaches relying on fixed image formation pipelines, the capabilities of smartphones using co
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Signal-Aware Broadband Doa Estimation Using Attention Mechanisms
We refer to direction-of-arrivals (DOAs) estimation of a user-defined subset of directional (desired) sound sources as signal-aware DOA estimation. Source selection, thereby, can be achieved with time-frequency masks to apply attention to TF bins dominate
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Subspace-Based Speech Correlation Vector Estimation For Single-Microphone Multi-Frame Mvdr Filtering
Aiming at exploiting the speech correlation across consecutive time-frames in the short-time Fourier transform domain, the multi-frame minimum variance distortionless response (MFMVDR) filter for single-microphone speech enhancement has been proposed. Thi
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Revisit Of Estimate Sequence For Accelerated Gradient Method
In this paper, we revisit the problem of minimizing a convex function $f(mathbf{x})$ with Lipschitz continuous gradient via accelerated gradient methods (AGM). To do so, we consider the so-called estimate sequence (ES), a useful analysis tool for establi
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Feature Drift Resilient Tracking Of The Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion
An analysis of the motion of the common carotid artery (CCA) provides effective indicators for cardiovascular diseases. Here, we propose a method for tracking CCA wall motion from a B-mode ultrasound video sequence. An unscented Kalman filter based on a s
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Modeling The Environment In Deep Reinforcement Learning: The Case Of Energy Harvesting Base Stations
In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energy. We propose a centralized control algorithm based
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Spherical Video Coding With Geometry And Region Adaptive Transform Domain Temporal Prediction
Many virtual and augmented reality applications depend critically on efficient compression of spherical videos. Current approaches apply a projection geometry to map a spherical video onto the plane(s), wherein a standard codec can be used for compression
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Domain Robust, Fast, And Compact Neural Language Models
Despite advances in neural language modeling, obtaining a good model on a large scale multi-domain dataset still remains a difficult task. We propose training methods for building neural language models for such a task, which are not only domain robust, b
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Generalized Linear Bandits With Safety Constraints
The classical multi-armed bandit is a class of sequential decision making problems where selecting actions incurs costs that are sampled independently from an unknown underlying distribution. Bandit algorithms have many applications in safety critical sys
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A Hybrid Approach For Thermographic Imaging With Deep Learning
We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two archi
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Learning Differentiable Sparse And Low Rank Networks For Audio-Visual Object Localization
Parsimonious modelling, including sparsity and low rankness, has becomes a cornerstone in modern machine learning and signal processing. However, these modelling techniques have limited capabity to learn from large-scale data, and often require some pre-d
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Deep Learning-Based Beam Alignment In Mmwave Vehicular Networks
Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matri
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Deep Casa For Talker-Independent Monaural Speech Separation
Monaural speech separation is the task of separating target speech from interference in single-channel recordings. Although substantial progress has been made recently in deep learning based speech separation, previous studies usually focus on a single ty
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Stabilizing Multi-Agent Deep Reinforcement Learning By Implicitly Estimating Other Agents’ Behaviors
Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it?s power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and
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Learning Connectivity And Higher-Order Interactions In Radial Distribution Grids
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of
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Classification Of Depth And Surface Edges With Deep Features
Edges in 2D images fall into two categories: depth edges and surface edges, depending on if the edge corresponds to an abrupt change in depth (the distance from the camera). This edge type is an efficient, robust, and effective information in many applica
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Tensor-To-Vector Regression For Multi-Channel Speech Enhancement Based On Tensor-Train Network
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to
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A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation. However, aggregating contextual information from a high-resolution
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Resting-State Eeg-Based Biometrics With Signals Features Extracted By Multivariate Empirical Mode Decomposition
EEG-based biometrics has gained great attention in recent years due to its superiority over traditional biometrics in terms of its resistance to circumvention. While there are numerous choices of data acquisition protocol, the present study is carried out
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Training Deep Spiking Neural Networks For Energy-Efficient Neuromorphic Computing
Spiking Neural Networks (SNNs) encode input information temporally using sparse spiking events, which can be harnessed to achieve higher computational efficiency. However, considering the rapid strides in accuracy enabled by Analog Neural Networks (ANNs),
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Speech Emotion Recognition With Local-Global Aware Deep Representation Learning
Convolutional neural networks (CNN) based deep representation learning methods for speech emotion recognition (SER) have demonstrated great success. The basic design of CNN restricts the ability to model only local information well. Capsule network (CapsN
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Multichannel Active Noise Control With Spatial Derivative Constraints To Enlarge The Quiet Zone
Active noise control is an efficient approach in dealing with unwanted acoustic disturbances. However, most of the active noise control algorithms aim to control the signal of the error sensor leading to local noise attenuation only around the error micro
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Speaker Diarization With Session-Level Speaker Embedding Refinement Using Graph Neural Networks
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for dist
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Korean Singing Voice Synthesis Based On Auto-Regressive Boundary Equilibrium Gan
Singing voice synthesis is a generative task that involves not only multidimensional controls of a singer model such as phonetic modulation by lyrics and pitch control by music score but also expressive elements such as breath sounds and vibrato. Recently
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K-Space Trajectory Design For Reduced Mri Scan Time
The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous traject
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Intelligent Student Behavior Analysis System For Real Classrooms
In this paper, we design an intelligent student behavior analysis system for recorded classrooms, which automatically detects hand-raising, standing, and sleeping behaviors of students. Detecting these behaviors is quite challenging mainly due to various