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Neural Coding Strategies For Event-Based Vision Data
Neural coding schemes are powerful tools used within neuroscience. This paper introduces three different neural coding scheme formations for event-based vision data which are designed to emulate the neural behaviour exhibited by neurons under stimuli. Pre
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Online Channel Estimation For Hybrid Beamforming Architectures
Hybrid analog-/digital beamforming architectures are a promising means of reducing power consumption and hardware costs in large multi-antenna transceivers. However, channel estimation becomes more complicated compared with conventional (fully-digital) ar
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Tensor Decomposition-Based Beamspace Esprit Algorithm For Multidimensional Harmonic Retrieval
Beamspace processing is an efficient and commonly used approach in harmonic retrieval (HR). In the beamspace, measurements are obtained by linearly transforming the sensing data, thereby achieving a compromise between estimation accuracy and system comple
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Decidable Variable-Rate Dataflow For Heterogeneous Signal Processing Systems
Dynamic dataflow models of computation have become widely used through their adoption to popular programming frameworks such as TensorFlow and GNU Radio. Although dynamic dataflow models offer more programming freedom, they lack analyzability compared to
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A Neural Network For Monaural Intrusive Speech Intelligibility Prediction
Monaural intrusive speech intelligibility prediction (SIP) methods aim to predict the speech intelligibility (SI) of a single-microphone noisy and/or processed speech signal using the underlying clean speech signal. In the present work, we propose a neura
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Tracing Network Evolution Using The Parafac2 Model
Characterizing time-evolving networks is a challenging task, but it is crucial for understanding the dynamic behavior of complex systems such as the brain. For instance, how spatial networks of functional connectivity in the brain evolve during a task is
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Multi-Agent Deep Reinforcement Learning For Distributed Handover Management In Dense Mmwave Networks
The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communicati
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Detection Of Malicious Vbscript Using Static And Dynamic Analysis With Recurrent Deep Learning
Attackers have used malicious VBScripts as an important computer infection vector. In this study, we explore a system that employs both static and dynamic analysis to detect malicious VBScripts. For the static analysis, we investigate two deep recurrent m
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Media Classification With Bayesian Optimization And Vapnik-Chervonenkis (Vc) Bounds
The automatic classification of content is an essential requirement for multimedia applications. Present research for audio-based classifiers uses short- and long-term analysis of signals, with temporal and spectral features. In our prior study, we presen
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Prediction Of Individual Progression Rate In Parkinson’S Disease Using Clinical Measures And Biomechanical Measures Of Gait And Postural Stability
Parkinson?s disease (PD) is a common neurological disorder characterized by gait impairment. PD has no cure, and an impediment to developing a treatment is the lack of any accepted method to predict disease progression rate. The primary aim of this study
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Active Control Of Line Spectral Noise With Simultaneous Secondary Path Modeling Without Auxiliary Noise
Online secondary path modeling is appealing for most active noise control systems due to its benefit of effective tracking of the varying acoustic environment and possible variation of the control sources and sensors. However, the usually utilized additiv
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Anomaly Detection In Mixed Time-Series Using A Convolutional Sparse Representation With Application To Spacecraft Health Monitoring
This paper introduces a convolutional sparse model for anomaly detection in mixed continuous and discrete data. This model, referred to as C-ADDICT, builds upon the experiences of our previous ADDICT algorithm. It can handle discrete and continuous data j
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Manifold Gradient Descent Solves Multi-Channel Sparse Blind Deconvolution Provably And Efficiently
Multi-channel sparse blind deconvolution refers to the problem of learning an unknown filter by observing its circulant convolutions with multiple input signals that are sparse. It is challenging to learn the filter efficiently due to the bilinear structu
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Oov Recovery With Efficient 2Nd Pass Decoding And Open-Vocabulary Word-Level Rnnlm Rescoring For Hybrid Asr
In this paper, we investigate out-of-vocabulary (OOV) word recovery in word-based hybrid automatic speech recognition (ASR) systems, with emphasis on dynamic vocabulary expansion for both Weight Finite State Transducer (WFST)-based decoding and word-level
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Fast Clustering With Co-Clustering Via Discrete Non-Negative Matrix Factorization For Image Identification
How to effectively cluster large-scale image data sets is a challenge and is receiving more and more attention. To address this problem, a novel clustering method called fast clustering with co-clustering via discrete non-negative matrix factorization, is
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Apb2Face: Audio-Guided Face Reenactment With Auxiliary Pose And Blink Signals
Audio-guided face reenactment aims at generating photorealistic faces using audio information while maintaining the same facial movement as when speaking to a real person. However, existing methods can not generate vivid face images or only reenact low-re
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Predicting Performance Outcome With A Conversational Graph Convolutional Network For Small Group Interactions
Studying behaviors of members during small group interaction provides objective insights in improving the efficiency of the decision making process in our daily working life. By introducing the use of the graph structure in modeling the natural inter-memb
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Eliminating Out-Of-Cell Interference In Cellular Massive Mimo With A Single Additional Transceiver
Wireless cellular communication networks are bandwidth and interference limited. An important means to overcome these resource limitations is the use of multiple antennas. Base stations equipped with a very large (massive) number of antennas have been the
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Cpwc: Contextual Point Wise Convolution For Object Recognition
Convolutional layers are a major driving force behind the successes of deep learning. Pointwise convolution (PWC) is a 1x1 convolutional filter that is primarily used for parameter reduction. However, the PWC ignores the spatial information around the poi
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A Switching Transmission Game With Latency As The User's Communication Utility
We consider the communication between a source (user) and a destination in the presence of a jammer, and study resource assignment in a non-cooperative game theory framework using communication latency as the user's utility. The user switches between two
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Distributed Wave-Domain Active Noise Control Based On The Diffusion Strategy
Conducting the spatial active noise control (ANC) in wave-domain has been shown advantageous over conventional point-based methods. In the existing schemes, signals at all error microphones are collected and processed in a centralized manner to update the
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Extrapolated Alternating Algorithms For Approximate Canonical Polyadic Decomposition
Tensor decompositions have become a central tool in machine learning to extract interpretable patterns from multiway arrays of data. However, computing the approximate Canonical Polyadic Decomposition (aCPD), one of the most important tensor decomposition
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Fast Start-Up Algorithm For Adaptive Noise Cancellers With Novel Snr Estimation And Stepsize Control
This paper proposes a fast convergence algorithm for adaptive noise cancellers with novel SNR (signal-to-noise ratio) estimation and stepsize control. The stepsize for coefficient adaptation is controlled with an estimated SNR for low distortion of the ou
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Defending Graph Convolutional Networks Against Adversarial Attacks
The interconnection of social, email, and media platforms enables adversaries to manipulate networked data and promote their malicious intents. This paper introduces graph neural network architectures that are robust to perturbed networked data. The novel
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Improving Universal Sound Separation Using Sound Classification
Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification. Most audio source separation approaches focus only on separating sources belonging to a restricted domain of source classes, s
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Private Fl-Gan: Differential Privacy Synthetic Data Generation Based On Federated Learning
Generative Adversarial Network (GAN) has already made a big splash in the field of generating realistic ``fake'' data. However, when data is distributed and data-holders are reluctant to share data for privacy reasons, GAN's training is difficult. To addr
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Gfcn: A New Graph Convolutional Network Based On Parallel Flows
In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory. In
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Towards Fast And Accurate Streaming End-To-End Asr
End-to-end (E2E) models fold the acoustic, pronunciation and language models of a conventional speech recognition model into one neural network with a much smaller number of parameters than a conventional ASR system, thus making it suitable for on-device
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Dnn-Based Mask Estimation Integrating Spectral And Spatial Features For Robust Beamforming
Spectral mask based beamforming has showed competitive performance on multi-channel speech enhancement in recent years. However, such methods apply mask estimation on each channel and ensemble the masks from multiple channels into one for speech and noise
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Pitchnet: Unsupervised Singing Voice Conversion With Pitch Adversarial Network
Singing voice conversion is to convert a singer's voice to another one's voice without changing singing content. Recent work shows that unsupervised singing voice conversion can be achieved with an autoencoder-based approach cite{nachmani2019unsupervised
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A Sparse Linear Array Approach In Automotive Radars Using Matrix Completion
We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA be
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End-End Speech-To-Text Translation With Modality Agnostic Meta-Learning
Collecting large amounts of data to train end-to-end Speech Translation (ST) models is more difficult compared to the ASR and MT tasks. Previous studies have proposed the use of transfer learning approaches to overcome the above difficulty. These approach
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Distributed Verification Of Belief Precisions Convergence In Gaussian Belief Propagation
Gaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions
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Array-Geometry-Aware Spatial Active Noise Control Based On Direction-Of-Arrival Weighting
Active noise control (ANC) over a sizeable space ideally requires uniformly distributed sensors and secondary sources, which limits the feasibility of practically realizing such systems. In this paper, we propose a direction of arrival (DOA) weighting alg
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Static Visual Spatial Priors For Doa Estimation
As we interact with the world, for example when we communicate with our colleagues in a large open space or meeting room, we continuously analyse the surrounding environment and, in particular, localise and recognise acoustic events. While we largely take
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Controlling The Perceived Sound Quality For Dialogue Enhancement With Deep Learning
Speech enhancement attenuates interfering sounds in speech signals but may introduce artifacts that perceivably deteriorate the output signal. We propose a method for controlling the trade-off between the attenuation of the interfering background signal a
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A Self-Attentive Emotion Recognition Network
Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel
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Reinforced Depth-Aware Deep Learning For Single Image Dehazing
Image dehazing continues to be one of the most challenging inverse problems. However, most deep learning-based methods usually design a regression network as a black-box tool to either estimate the dehazed image and/or the physical parameters in the haze
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Arnet:Attention-Based Refinement Network For Few-Shot Semantic Segmentation
Semantic segmentation is a challenging task for computer vision which aims to classify the objects from the pixel level. Previous methods based on deep learning have made some progress but the labeling work is very time-consuming. Few-shot semantic segmen
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Flow-Tts: A Non-Autoregressive Network For Text To Speech Based On Flow
In this work, we propose Flow-TTS, a non-autoregressive end-to-end neural TTS model based on generative flow. Unlike other non-autoregressive models, Flow-TTS can achieve high-quality speech generation by using a single feed-forward network. To our knowle
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Temporal Coding In Spiking Neural Networks With Alpha Synaptic Function
We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike. This temporal coding scheme allows the supervised training of the net
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A Low-Resolution Adc Proof-Of-Concept Development For A Fully-Digital Millimeter-Wave Joint Communication-Radar
A fully-digital mmWave wideband JCR places difficult demands of power consumption and hardware complexity on the receivers' analog-to-digital converters (ADCs). To address these concerns, we present a low-complexity proof-of-concept (PoC) development for
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Speaker extraction aims to separate a target speaker from multiple voices which is useful for applications, e.g. teleconference. In many practical cases, it has an opportunity to get a piece voice of the target speaker in advance, which provides useful in
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Joint Sparse Recovery Using Deep Unfolding With Application To Massive Random Access
We propose a learning-based joint sparse recovery method for the multiple measurement vector (MMV) problem using deep unfolding. We unfold an iterative alternating direction method of multipliers (ADM) algorithm for MMV joint sparse recovery algorithm int
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Wideband Direction Of Arrival Estimation With Sparse Linear Arrays
This paper concerns wideband direction of arrival (DoA) estimation with sparse linear arrays (SLAs). We rely on the assumption that the power spectrum of the wideband sources is the same up to a scaling factor, which could in theory allow us to resolve no
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Graphical Evolutionary Game Theoretic Analysis Of Super Users In Information Diffusion
In social networks, to better understand the avalanche of information flow over networks and to investigate its impact on economy and our social life, it is of crucial importance to model and analyze the information diffusion process. To address the exist
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Quickest Change Detection In Anonymous Heterogeneous Sensor Networks
The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are $n$ heterogeneous sensors and a fusion center. The sensors are clustered into $K$ groups, and different groups follow different data generating
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Kalm: Key Area Localization Mechanism For Abnormality Detection In Musculoskeletal Radiographs
Recently abnormality detection in musculoskeletal radiographs has attracted many attentions. For abnormality detection, it is crucial to locate the most important area in the musculoskeletal radiographs. To achieve this goal, we propose a key area localiz