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Decoding 5G-Nr Communications Via Deep Learning
Upcoming modern communications are based on 5G specifications and aim at providing solutions for novel vertical industries. One of the major changes of the physical layer is the use of Low-Density Parity-Check (LDPC) code for channel coding. Although LDPC
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Teaching Signals And Systems - A First Course In Signal Processing
Signals and systems is a well known fundamental course in signal processing. How this course is taught to a student can spell the difference between whether s/he pursues a career in this field or not. Giving due consideration to this matter, this paper re
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Looking Enhances Listening: Recovering Missing Speech Using Images
Speech is understood better by using visual context; for this reason, there have been many attempts to use images to adapt automatic speech recognition (ASR) systems. Current work, however, has shown that visually adapted ASR models only use images as a r
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A Hierarchical Model For Dialog Act Recognition Considering Acoustic And Lexical Context Information
Dialog act recognition (DAR) is important to capture speakers' intention in a dialog system. Traditional methods commonly use the lexical information from transcripts, acoustic information from speech, and dialog context information to do DAR. However, in
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A Low-Complexity Map Detector For Distributed Networks
This work describes a generalization of our previous maximum likelihood (ML) detector to a maximum a posteriori (MAP) detector in distributed networks using the diffusion LMS algorithm. Nodes in the network must decide between two concurrent hypotheses co
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Non-Parametric Community Change-Points Detection In Streaming Graph Signals
Detecting changes in network-structured time series data is of utmost importance in critical applications as diverse as detecting denial of service attacks against online service providers or monitoring energy and water supplies. The aim of this paper is
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Detect Insider Attacks Using Cnn In Decentralized Optimization
This paper studies the security issue of a gossip-based distributed projected gradient (DPG) algorithm, when it is applied for solving a decentralized multi-agent optimization. It is known that the gossip-based DPG algorithm is vulnerable to insider attac
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What Is Best For Spoken Language Understanding: Small But Task-Dependant Embeddings Or Huge But Out-Of-Domain Embeddings?
Word embeddings are shown to be a great asset for several Natural Language and Speech Processing tasks. While they are already evaluated on various NLP tasks, their evaluation on spoken or natural language understanding (SLU) is less studied. The goal of
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Robust Hybrid Precoding For Interference Exploitation In Massive Mimo Systems
In this paper, we consider a multiuser massive MIMO system with hybrid analog-digital precoding architecture. The phase shifters in the hybrid precoding architecture are assumed to be imperfect, where the true values of both phase and magnitude of the pha
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Estimating Centrality Blindly From Low-Pass Filtered Graph Signals
This paper considers blind methods for centrality estimation from graph signals. We model graph signals as the outcome of an unknown low-pass graph filter excited with influences governed by a sparse sub-graph. This model is compatible with a number of da
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Opportunistic Use Of Gnss Signals To Characterize The Environment By Means Of Machine Learning Based Processing
GNSS is widely used to provide positions in an absolute reference frame in Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), where GNSS is merged with the information provided by other sensors. Even if the main goal of GNSS signal process
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Human-Machine Collaboration For Medical Image Segmentation
Image segmentation is a ubiquitous step in almost any medical image study. Deep learning-based approaches achieve state-of-the-art in the majority of image segmentation benchmarks. However, end-to-end training of such models requires sufficient annotation
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Data-Driven Wind Speed Estimation Using Multiple Microphones
A deep neural network (DNN) based approach for estimating the speed of airflows using closely-spaced microphones is proposed. The spatial characteristics of wind noise measured with a small-aperture array are exploited, i.e., the low-frequency spatial coh
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Unsupervised Pretraining Transfers Well Across Languages
Cross-lingual and multi-lingual training of Automatic Speech Recognition (ASR) has been extensively investigated in the supervised setting. This assumes the existence of a parallel corpus of speech and orthographic transcriptions. Recently, contrastive pr
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Object Surface Estimation From Radar Images
In this paper we develop a deep neural network (DNN) method for estimating the object surface from radar 2D image (azimuth-range). The DNN is designed to maintain the input image angular resolution and produces two outputs per each angle, which are a clas
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On The Effect Of Reflectance On Phasor Field Non-Line-Of-Sight Imaging
Non-line-of-sight (NLOS) imaging aims to visualize a occluded scene by exploiting its indirect reflections on visible surfaces. Previous methods approach this problem inverting the light transport on the hidden scene, but are limited to isolated, diffuse
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Learning Domain Invariant Representations For Child-Adult Classification From Speech
Diagnostic procedures for ASD (autism spectrum disorder) involve semi-naturalistic interactions between the child and a clinician. Computational methods to analyze these sessions require an end-to-end speech and language processing pipeline that go from r
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Multimodal Violence Detection In Videos
Effective tools for detection of violence are highly demanded, specially when dealing with video streams. Such tools have a wide range of applications, from forensics and law enforcement to parental control over the ever increasing amount of videos availa
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The Swax Benchmark: Attacking Biometric Systems With Wax Figures
A face spoofing attack occurs when an intruder attempts to impersonate someone who carries a gainful authentication clearance. It is a trending topic due to the increasing demand for biometric authentication on mobile devices, high-security areas, among o
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Nasil : Neural Architecture Search With Imitation Learning
Automated machine learning (AML) refers to a class of techniques that, given a problem, can find an optimal set of model architectures, properties, and parameters. In recent years, AML has shown great success in finding neural network structures that are
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An Enhanced Decoding Algorithm For Coded Compressed Sensing
Coded compressed sensing is an algorithmic framework tailored to sparse recovery in very large dimensional spaces. This framework is originally envisioned for the unsourced multiple access channel, a wireless paradigm attuned to machine-type communication
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Semi-Regular Geometric Kernel Encoding & Reconstruction For Video Compression
Conventional video coding schemes employ a hybrid motion prediction / residual transform coding paradigm, which only exploits redundancy in individual pairs of video frames for compression gain. However, rigid geometric structures in 3D space---e.g., a bu
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Feedback Turbo Autoencoder
Designing channel codes is one of the core research areas for modern communication systems. Canonical channel codes asymptotically achieve near-capacity performance under a large block length regime for additive white gaussian noise channels. However, thi
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Neural Oracle Search On N-Best Hypotheses
In this paper, we propose a neural search algorithm to select the most likely hypothesis using a sequence of acoustic representations and multiple hypotheses as input. The algorithm provides a sequence level score for each audio-hypothesis pair that is ob
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Learn-By-Calibrating: Using Calibration As A Training Objective
Calibration error is commonly adopted for evaluating the quality of uncertainty estimators in deep neural networks. In this paper, we argue that such a metric is highly beneficial for training predictive models, even when we do not explicitly measure the
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Privacy-Preserving Image Sharing Via Sparsifying Layers On Convolutional Groups
We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive co
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Simple Caching Schemes For Non-Homogeneous Miso Cache-Aided Communication Via Convexity
We present a novel scheme for cache-aided communication over multiple-input and single output (MISO) cellular networks. The presented scheme achieves the same number of degrees of freedom as known coded caching schemes, but, at much lower complexity. The
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Feedback Recurrent Autoencoder
In this work, we propose a new recurrent autoencoder architecture, termed Feedback Recurrent AutoEncoder (FRAE), for online compression of sequential data with temporal dependency. The recurrent structure of FRAE is designed to efficiently extract the red
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Accelerating Linear Algebra Kernels On A Massively Parallel Reconfigurable Architecture
Much of the recent work on domain-specific architectures has focused on bridging the gap between performance/efficiency and programmability. We consider one such example architecture, Transformer, consisting of light-weight cores interconnected by caches
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Arsm Gradient Estimator For Supervised Learning To Rank
We propose a new model for supervised learning to rank. In our model, the relevance labels are assumed to follow a categorical distribution whose probabilities are constructed based on a scoring function. We optimize the training objective with respect to
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Dynamically Modulated Deep Metric Learning For Visual Search
This paper propose dynamically modulated metric learning (DMML) for learning a tiered similarity space to perform visual search. Existing methods often treat the training samples having different degree of information with equal importance which hinders i
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Accurate Semidefinite Relaxation Method For 3-D Rigid Body Localization Using Aoa
This paper addresses the rigid body localization problem using angle-of-arrival measurements. We formulate the problem as a constrained weighted least squares (CWLS) minimization problem with the rotation matrix and position vector as variables, which is
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Joint Optimization Of Sampling Patterns And Deep Priors For Improved Parallel Mri
Multichannel imaging techniques are widely used in MRI to reduce the scan time. These schemes typically perform undersampled acquisition and utilize compressed-sensing based regularized reconstruction algorithms. Model-based deep learning (MoDL) framework
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Overlap Local-Sgd: An Algorithmic Approach To Hide Communication Delays In Distributed Sgd
Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variability such as high communication delay or random node slowdown greatly impedes
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Frame-Level Phoneme-Invariant Speaker Embedding For Text-Independent Speaker Recognition On Extremely Short Utterances
This paper investigates a phoneme-invariant speaker embedding approach for speaker recognition on extremely short utterances. Intuitively, phonemes are nuisance information for text-independent speaker recognition task since the contents of the speech are
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Normalized Least-Mean-Square Algorithms With Minimax Concave Penalty
We propose a novel problem formulation for sparsity-aware adaptive filtering based on the nonconvex minimax concave (MC) penalty, aiming to obtain a sparse solution with small estimation bias. We present two algorithms: the first algorithm uses a single f
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Improving Efficiency In Large-Scale Decentralized Distributed Training
Decentralized Parallel SGD (D-PSGD) and its asynchronous variant Asynchronous Parallel SGD (AD-PSGD) is a family of distributed learning algorithms that have been demonstrated to perform well for large-scale deep learning tasks. One drawback of (A)D-PSGD
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Supervised Deep Hashing For Efficient Audio Event Retrieval
Efficient retrieval of audio events can facilitate real-time implementation of numerous query and search-based systems. This work investigates the potency of different hashing techniques for efficient audio event retrieval. Multiple state-of-the-art weak
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Multimodal Speaker Diarization Of Real-World Meetings Using D-Vectors With Spatial Features
Deep neural network based audio embeddings (d-vectors) have demonstrated superior performance in audio-only speaker diarization compared to traditional acoustic features such as mel-frequency cepstral coefficients (MFCCs) and i-vectors. However, there has
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Language Independent Gender Identification From Raw Waveform Using Multi-Scale Convolutional Neural Networks
In this work, we propose a raw waveform based multi- scale convolution neural network approach for language- independent gender identification. Our approach uses raw audio waveform as input to the 1-dimensional multi-scale convolutional neural network ins
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End-To-End Multi-Speaker Speech Recognition With Transformer
Recently, fully recurrent neural network (RNN) based end-to-end models have been proven to be effective for multi-speaker speech recognition in both the single-channel and multi-channel scenarios. In this work, we explore the use of Transformer models for
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A Model Of Double Descent For High-Dimensional Logistic Regression
We consider a model for logistic regression where only a subset of features of size $p$ is used for training a linear classifier over $n$ training samples. The classifier is obtained by running gradient-descent (GD) on logistic-loss. For this model, we in
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Low-Complexity Levenberg-Marquardt Algorithm For Tensor Canonical Polyadic Decomposition
In this paper, we propose CPD-fLM++, a fast implementation of the Levenberg-Marquardt (LM) algorithm for the tensor canonical polyadic decomposition. The overall algorithmic framework follows exactly the LM approach, which enjoys locally a super-linear co
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The Discrete Stockwell Transforms For Infinite-Length Signals And Their Real-Time Implementations
The various forms of the Stockwell transforms (ST) introduced in the literature have been developed for off-line signal processing on finite-length signals. However, in many applications such as audio, medical or radar signal processing, signals to be ana
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Graph Vertex Sampling With Arbitrary Graph Signal Hilbert Spaces
Graph vertex sampling set selection aims at selecting a set of vertices of a graph such that the space of graph signals that can be reconstructed exactly from those samples alone is maximal. In this context, we propose to extend sampling set selection bas
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Rethinking Retinal Landmark Localization As Pose Estimation: Naive Single Stacked Network For Optic Disk And Fovea Detection
Automatic detection of optic disk and fovea, the two fundamental biological landmarks of the retinal system, is crucial to track the disease progression in a diabetic patient. Recent advances in this direction were mostly limited to applying CNN based net
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Dnn-Based Speech Recognition For Globalphone Languages
This paper describes new reference benchmark results based on hybrid Hidden Markov Model and Deep Neural Networks (HMM-DNN) for the GlobalPhone (GP) multilingual text and speech database. GP is a multilingual database of high-quality read speech with corr
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Efficient Bird Sound Detection On The Bela Embedded System
Monitoring wildlife is an important aspect of conservation initiatives. Deep learning detectors can help with this, although it is not yet clear whether they can run efficiently on an embedded system in the wild. This paper proposes an automatic detection
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Deep Rainrate Estimation From Highly Attenuated Downlink Signals Of Ground-Based Communications Satellite Terminals
While the use of weather radars to continuously monitor the spatio-temporal dynamics of precipitation has grown in recent years, these systems are expensive and sparsely deployed across the world. In this context, densely located ground-based terminals fo