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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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Fusionndvi: A Novel Fusion Method For Ndvi In Remote Sensing
Normalized difference vegetation index (NDVI) is widely utilized to examine vegetation coverage and estimate crop yield. To obtain a high-resolution (HR) NDVI, fusion techniques, which first generates a HR multispectral (MS) image by fusing a low-resoluti
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Dynamic Resource Optimization And Altitude Selection In Uav-Based Multi-Access Edge Computing
The aim of this work is to develop a dynamic optimization strategy to allocate communication and computation resources in a Multi-access Edge Computing (MEC) scenario, where Unmanned AerialVehicles (UAVs) act as flying base station platforms endowed with
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Cp-Gan: Context Pyramid Generative Adversarial Network For Speech Enhancement
The topic of speech enhancement has been largely improved recently, especially with the development of generative adversarial networks (GANs). However prior methods simply follow the GAN architectures from computer vision tasks without specific designs fo
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Towards Linking The Lakh And Imslp Datasets
This paper investigates the problem of matching a MIDI file against a large database of piano sheet music images. Previous sheet-audio and sheet-MIDI alignment approaches have primarily focused on a 1-to-1 alignment task, which is not a scalable solution
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Efficient Shallow Wavenet Vocoder Using Multiple Samples Output Based On Laplacian Distribution And Linear Prediction
This paper presents a novel way for an efficient implementation scheme of shallow WaveNet vocoder with multiple samples (segment) output based on the use of Laplacian distribution and linear prediction. In our previous work, we have proposed a shallow arc
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End-To-End Spoken Language Understanding Without Matched Language Speech Model Pretraining Data
In contrast to conventional approaches to spoken language understanding (SLU) that consist of cascading a speech recognizer with a natural language understanding component, end-to-end (E2E) approaches for SLU infer semantics directly from the speech signa
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Two-Dimensional Doa Estimation For Coprime Planar Array: A Coarray Tensor-Based Solution
Coprime arrays can cope with the underdetermined case for direction-of-arrival (DOA) estimation. However, the popular matrix-based coarray signal processing approaches suffer performance loss on the underlying characteristics among the multi-dimensional s
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Voice Activity Detection For Transient Noisy Environment Based On Diffusion Nets
We address voice activity detection in acoustic environments of transients and stationary noises, which often occur in real-life scenarios. We exploit unique spatial patterns of speech and non-speech audio frames by independently learning their underlying
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Chronological Age Estimation Under The Guidance Of Age-Related Facial Attributes
Although the researches of facial attributes' analysis have been launched for decades, the estimation of chronological age attribute remains a big challenge. Previous researchers have found that some facial attributes (e.g., gender and race attributes) ha
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Real-Time Acoustic Scene Classification For Hearing Aids
Acoustic scene classification is a popular topic mostly combining the fields of audio signal processing and machine learning. Particularly the detection and classification of acoustic scenes and events (DCASE) challenge, which is held each year, increased
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IEEE ICASSP 2020 - State of the Society, Town Hall
IEEE ICASSP 2020 - State of the Society, Town Hall, by Dr. Ahmed Tewfik, May 2020.
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Moga: Searching Beyond Mobilenetv3
The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods main
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A Geometric Approach For Unsupervised Similarity Learning
Metric learning groups similar examples together, while moving away dissimilar ones. This is a crucial task in image processing and computer vision. However, existing metric learning approaches require huge number of labeled examples for their success. In
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Enhanced Adversarial Strategically-Timed Attacks Against Deep Reinforcement Learning
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e.g., aut
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Genetic Algorithm Optimized Support Vector Machine In Noma-Based Satellite Networks With Imperfect Csi
With the help of a power-domain non-orthogonal multiple access (NOMA) scheme, satellite networks can simultaneously serve multiple users within limited time/spectrum resource block. However, the existence of channel estimation errors inevitably degrade th
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Statistical Signal Processing Approach For Rain Estimation Based On Measurements From Network Management Systems
In this paper we apply statistical signal processing methodologies on a real-world application of using Commercial Microwave Links (CMLs) as opportunistic sensors for rain monitoring. We formulate an appropriate parameter estimation problem, taking advant
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Low Complexity Nlms For Multiple Loudspeaker Acoustic Echo Canceller Using Relative Loudspeaker Transfer Functions
Speech signals captured by a microphone mounted to a smart soundbar or speaker are inherently contaminated by echos. Modern smart devices are usually characterized by low computational capabilities and low memory resources; in these cases, a low-complexit
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Fcem: A Novel Fast Correlation Extract Model For Real Time Steganalysis Of Voip Stream Via Multi-Head Attention
Extracting correlation features between codes-words with high computational efficiency is crucial to steganalysis of Voice over IP (VoIP) streams. In this paper, we utilized attention mechanisms, which have recently attracted enormous interests due to the
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Secure Symbol-Level Miso Precoding
While constructive interference offers indirect advantages in physical layer security by reducing the transmit power required to achieve a desired performance level, additional gains are possible by choosing the symbols to degrade the eavesdropper's abili
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On Binary Sequence Set Design With Applications To Automotive Radar
We consider herein the case of two vehicles equipped with multi-input multi-output (MIMO) automotive radars driving next to each other. We assume that 5G communications allow us to coordinate the radar probing waveforms for the vehicles. Then the binary s
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A Fast And Accurate Super-Resolution Network Using Progressive Residual Learning
Single-image super-resolution (SISR) task has witnessed great strides in the past few years with the development of deep learning. However, most existing studies concentrate on exploiting much deeper super-resolution networks, which are not friendly to th
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Pyannote.Audio: Neural Building Blocks For Speaker Diarization
We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-end neural building blocks that can be combined and jointly optimized to buil
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Overdetermined Independent Vector Analysis
We address the convolutive blind source separation problem for the (over-)determined case where (i) the number of nonstationary target-sources K is less than that of microphones M, and (ii) there are up to M - K stationary Gaussian noises that need not to
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Dnn-Chip Predictor: An Analytical Performance Predictor For Dnn Accelerators With Various Dataflows And Hardware Architectures
The recent breakthroughs in deep neural networks (DNNs) have spurred a tremendously increased demand for DNN accelerators. However, designing DNN accelerators is non-trivial as it often takes months/years and requires cross-disciplinary knowledge. To enab
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Objective Bayesian Detection Under Spatially Correlated Gaussian Observations For Multi-Antenna Cognitive Radio Network
This paper develops an objective Bayesian detector for asserting the presence of primary user (PU) signal buried in additive noise/interference using a sequence of complex vector samples from a multi-antenna spectrum sensing system. The PU signal is zero
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Towards A New Understanding Of The Training Of Neural Networks With Mislabeled Training Data
We investigate the problem of machine learning with mislabeled training data. We try to make the effects of mislabeled training better understood through analysis of the basic model and equations that characterize the problem. This includes results about
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Action-Manipulation Attacks On Stochastic Bandits
As stochastic multi-armed bandit model has many important applications, understanding the impact of adversarial attacks on this model is essential for the safe applications of this model. In this paper, we propose a new class of attack named action-manipu
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Improving Speaker-Attribute Estimation By Voting Based On Speaker Cluster Information
This paper proposes a general post-processing method for improving speaker-attribute estimation. Estimating speaker-specific attributes such as age and gender is an important task with a wide range of applications. While the recent proposed deep neural ne
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Generalized Kernel-Based Dynamic Mode Decomposition
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that
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Optimized Single Carrier Transceiver For Future Sub-Terahertz Applications
The performance of sub-THz communications, contemplated for the next generation of wireless networks, are significantly degraded by oscillator phase noise. In this paper, we address the design of a single carrier transceiver resilient to phase noise. This
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Asymptotic Stochastic Analysis Of Partially Relaxed Dml
The Partial Relaxation (PR) approach has recently been proposed to solve the Direction of Arrival (DoA) estimation problem. In this paper, we investigate the outlier production mechanism of the Partially Relaxed Deterministic Maximum Likelihood (PR-DML) D
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Overcoming High Nanopore Basecaller Error Rates For Dna Storage Via Basecaller-Decoder Integration And Convolutional Codes
As magnetization and semiconductor based storage technologies approach their limits, bio-molecules, such as DNA, have been identified as promising media for future storage systems, due to their high storage density (petabytes/gram) and long-term durabilit
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Multilingual Grapheme-To-Phoneme Conversion With Byte Representation
Grapheme-to-phoneme (G2P) models convert a written word into its corresponding pronunciation and are essential components in automatic-speech-recognition and text-to-speech systems. Recently, the use of neural encoder-decoder architectures has substantial