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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
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A Fast Reduced-Rank Sound Zone Control Algorithm Using The Conjugate Gradient Method
Sound zone control enables different users to enjoy different audio contents in the same acoustic environment. Generalized eigenvalue decomposition (GEVD)-based methods allow us to control the trade-off between the acoustic contrast (AC) and signal distor
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Sequential Vessel Trajectory Identification Using Truncated Viterbi Algorithm
In this work, we propose a novel classification algorithm that used to classify vessel data points into different trajectories. The algorithm is a truncated version of the Viterbi Algorithm. A physical model utilizing the observation information is used t
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Efficient Belief Propagation For Graph Matching
In this short note we derive a novel belief propagation algorithm for graph matching and we numerically evaluate it in the context of matching random graphs. The derived algorithm has a lower asymptotic time-complexity without significantly compromising t
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Volume Reconstruction For Light Field Microscopy
Light Field Microscopy is a 3D imaging technique that captures volumetric information in a single snapshot. It is appealing in microscopy because of its simple implementation and the peculiarity that it is much faster than methods involving scanning. Howe
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Generating Synthetic Audio Data For Attention-Based Speech Recognition Systems
Recent advances in text-to-speech (TTS) led to the development of flexible multi-speaker end-to-end TTS systems. We extend state-of-the-art attention-based automatic speech recognition (ASR) systems with synthetic audio generated by a TTS system trained o
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Favorable Propagation And Linear Multiuser Detection For Distributed Antenna Systems
Cell-free MIMO, employing distributed antenna systems (DAS), is a promising approach to deal with the capacity crunch of next generation wireless communications. In this paper, we consider a wireless network with transmit and receive antennas distributed
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Optimal Power Flow Using Graph Neural Networks
Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is m
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Sequential Methods For Detecting A Change In The Distribution Of An Episodic Process
A new class of stochastic processes called episodic processes is introduced to model the statistical regularity of data observed in several applications in cyberphysical systems, neuroscience, and medicine. Algorithms are proposed to detect a change in th
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Riemannian Framework For Robust Covariance Matrix Estimation In Spiked Models
This paper aims at providing an original Riemannian geometry to derive robust covariance matrix estimators in spiked models (i.e. when the covariance matrix has a low-rank plus identity structure). The considered geometry is the one induced by the product
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Atrial Fibrillation Risk Prediction From Electrocardiogram And Related Health Data With Deep Neural Network
Electrocardiography (ECG) is a widely used tool for studying and diagnosing the heart diseases. Atrial fibrillation (AF) is an irregular and often rapid heart rate that can increase the risk of strokes, heart failure and other heart-related complications.
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Phylogenetic Minimum Spanning Tree Reconstruction Using Autoencoders
The history of a shared and re-posted multimedia content can be reconstructed by analyzing the mutual relations between all of its near-duplicate copies and solving a minimum spanning tree (MST) problem, as shown by multimedia phylogeny research field. Un
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Deep James-Stein Neural Networks For Brain-Computer Interfaces
Nonparametric regression has proven to be successful in extracting features from limited data in neurological applications. However, due to data scarcity, most brain-computer interfaces still rely on linear classifiers. This work leverages the robustness
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Speech Recognition Model Compression
Deep Neural Network-based speech recognition systems are widely used in most speech processing applications. To achieve better model robustness and accuracy, these networks are constructed with millions of parameters, making them storage and compute-inten
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Spatial Gating Strategies For Graph Recurrent Neural Networks
Graph Recurrent Neural Networks (GRNNs) are a neural network architecture devised to learn from graph processes, which are time sequences of graph signals. Similarly to traditional recurrent neural networks, GRNNs experience the problem of vanishing/explo
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Deep Learning Based Prediction Of Hypernasality For Clinical Applications
Hypernasality refers to the perception of excessive nasal resonance during the production of oral sounds. Existing methods for automatic assessment of hypernasality from speech are based on machine learning models trained on disordered speech databases ra
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Speech-Driven Facial Animation Using Polynomial Fusion Of Features
Speech-driven facial animation involves using a speech signal to generate realistic videos of talking faces. Recent deep learning approaches to facial synthesis rely on extracting low-dimensional representations and concatenating them, followed by a decod
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Secl-Umons Database For Sound Event Classification And Localization
We introduce the SECL-UMons dataset for sound event classification and localization in the context of office environments. The multichannel dataset is composed of 11 event classes recorded at several realistic positions in two different rooms. The dataset
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Disentangled Speech Embeddings Using Cross-Modal Self-Supervision
The objective of this paper is to learn representations of speaker identity without access to manually annotated data. To do so, we develop a self-supervised learning objective that exploits the natural cross-modal synchrony between faces and audio in vid
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An Unsupervised Retinal Vessel Extraction And Segmentation Method Based On A Tube Marked Point Process Model
Retinal vessel extraction and segmentation is essential for supporting diagnosis of eye-related diseases. In recent years, deep learning has been applied to vessel segmentation and achieved excellent performance. However, these supervised methods require
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Super-Resolution Of 3D Color Point Clouds Via Fast Graph Total Variation
3D point clouds acquired by low-cost sensors are often in lower spatial resolutions than desired for rendering images on high-resolution displays. In this paper, we propose a fast super-resolution (SR) algorithm for color 3D point clouds. We first populat
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Concentration-Based Polynomial Calculations On Nicked Dna
In this paper, we introduce a novel scheme for computing polynomial functions on a substrate of nicked DNA. We first discuss a fractional encoding of data, based on the concentration of nicked double DNA strands. Then we show how to perform multiplication
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Soft-Output Finite Alphabet Equalization For Mmwave Massive Mimo
Next-generation wireless systems are expected to combine millimeter-wave (mmWave) and massive multi-user multiple-input multiple-output (MU-MIMO) technologies to deliver high data-rates. These technologies require the basestations (BSs) to process high-di
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Age-Based Scheduling Policy For Federated Learning In Mobile Edge Networks
Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained par
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Prototypical Networks For Small Footprint Text-Independent Speaker Verification
Speaker verification aims to recognize target speakers with very few enrollment utterances. Conventional approaches learn a representation model to extract the speaker embeddings for verification. Recently, there are several new approaches in meta-learnin
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2D-To-2D Mask Estimation For Speech Enhancement Based On Fully Convolutional Neural Network
In recent years, the deep learning-based approaches are popular in the field of singe-channel speech enhancement. Convolutional neural networks (CNNs) are a standard component of many current speech enhancement system. In this study, we design a new Fully
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Gated Mechanism For Attention Based Multimodal Sentiment Analysis
Multimodal sentiment analysis has recently gained popularity because of its relevance to social media posts, customer service calls and video blogs. In this paper, we address three aspects of multimodal sentiment analysis; 1. Cross modal interaction learn
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An Empirical Study Of Transformer-Based Neural Language Model Adaptation
We explore two adaptation approaches of deep Transformer based neural language models (LMs) for automatic speech recognition. The first approach is a pretrain-finetune framework, where we first pretrain a Transformer LM on a large-scale text corpus from s
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One-Shot Parametric Audio Production Style Transfer With Application To Frequency Equalization
Audio production is a difficult process for many people, and properly manipulating sound to achieve a certain effect is non-trivial. In this paper, we present a method that facilitates this process by inferring appropriate audio effect parameters in order
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Transformer Transducer: A Streamable Speech Recognition Model With Transformer Encoders And Rnn-T Loss
In this paper we present an end-to-end speech recognition model with Transformer encoders that can be used in a streaming speech recognition system. Transformer computation blocks based on self-attention are used to encode both audio and label sequences i
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Decentralized Min-Max Optimization: Formulations, Algorithms And Applications In Network Poisoning Attack
This paper discusses formulations and algorithms which allow a number of agents to collectively solve problems involving both (non-convex) minimization and (concave) maximization operations. These problems have a number of interesting applications in info
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Cross-Vae: Towards Disentangling Expression From Identity For Human Faces
Facial expression and identity are two independent yet intertwined components for representing a face. For facial expression recognition, identity can contaminate the training procedure by providing tangled but irrelevant information. In this paper, we pr
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Toso: Student's-T Distribution Aided One-Stage Orientation Target Detection In Remote Sensing Images
In this paper, a robust Student?s-T distribution aided One-Stage Orientation detector, namely TOSO, is proposed to address orientation target detection in remote sensing images. A one-stage keypoint based network architecture is used to avoid the complica
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Adversarial Example Detection By Classification For Deep Speech Recognition
Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regarding to adversary?s access level to the victim learning algorithm. To defen
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Aligntts: Efficient Feed-Forward Text-To-Speech System Without Explicit Alignment
Targeting at both high efficiency and performance, we propose AlignTTS to predict the mel-spectrum in parallel. AlignTTS is based on a Feed-Forward Transformer which generates mel-spectrum from a sequence of characters, and the duration of each character
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Weakly Supervised Segmentation Guided Hand Pose Estimation During Interaction With Unknown Objects
Hand pose estimation is important for human computer interaction, but the performance is not satisfying when the hand is interacting with objects. To alleviate the influence of unknown objects, we propose a novel weakly supervised segmentation guided sche
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Deep Geometric Knowledge Distillation With Graphs
In most cases deep learning architectures are trained disregarding the amount of operations and energy consumption. However, some applications, like embedded systems, can be resource-constrained during inference. A popular approach to reduce the size of a
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Audio-Assisted Image Inpainting For Talking Faces
The goal of our work is to complete missing areas of images of talking faces, exploiting information from both the visual and audio modalities. Existing image inpainting methods rely solely on visual content that doesn?t always provide sufficient informat
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Improving Spoken Question Answering Using Contextualized Word Representation
While question answering (QA) systems have witnessed great breakthroughs in reading comprehension (RC) tasks, spoken question answering (SQA) is still a much less investigated area. Previous work shows that existing SQA systems are limited by catastrophic
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Unsupervised Neural Mask Estimator For Generalized Eigen-Value Beamforming Based Asr
The state-of-art methods for acoustic beamforming in multi-channel ASR is based on a neural mask estimator that attempts to learn the prediction of speech and noise using a paired corpus of clean and noisy recordings (teacher model). In this paper, we att