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Showing 1901 - 1950 of 1951
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Ertis: Real-Time 3D Acoustic Sonar Imaging Using Sparse Microphone Arrays
In recent years, our research group has developed state of the art 3D sonar sensors which use a low-cost MEMS microphone array for real-time acoustic imaging in air. Using this sensor, various robotic applications have been developed, including obstacle a
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A Novel Method For Obtaining Diffuse Field Measurements For Microphone Calibration
NOVELTY OF THE DEMO: Is it possible to obtain a diffused field response of a microphone array and perform calibration in under a minute? If such a method exists, is it possible to achieve an accuracy of half a dB from the expected response? The answer to
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From Compressed Sensing to Deep Learning: Tasks, Structures, and Models
From Compressed Sensing to Deep Learning: Tasks, Structures, and Models.
Presenter: Yonina Eldar, ICASSP 2020.
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Attentive Item2Vec: Neural Attentive User Representations
Factorization methods for recommender systems tend to represent users as a single latent vector. However, user behavior and interests may change in the context of the recommendations that are presented to the user. For example, in the case of movie recomm
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Supervised Canonical Correlation Analysis Of Data On Symmetric Positive Definite Manifolds By Riemannian Dimensionality Reduction
Most computer vision problems entail data that reside on Riemannian manifolds. Canonical correlation analysis (CCA) is a powerful method that captures correlations between any two sets of matrices. In this paper, we propose a framework for a supervised CC
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Dynamic Oversampling In 1-Bit Quantized Asynchronous Large-Scale Multiple-Antenna Systems For Sustainable Iot Networks
In this paper, we propose a dynamic oversampling technique for asynchronous large-scale multiple-antenna systems with 1-bit analog-to-digital converters at the base station that is suitable for sustainable internet of things and cellular networks. To the
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Conditional Density Driven Grid Design In Point-Mass Filter
The paper is devoted to the state estimation of nonlinear stochastic dynamic systems. The stress is laid on a grid-based numerical solution to the Bayesian recursive relations using the point-mass filter (PMF). In the paper, a novel conditional density dr
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Camera Configuration Design In Cooperative Active Visual 3D Reconstruction: A Statistical Approach
Visual 3D reconstruction is an essential technique in computer vision which restores the 3D model of the scene from multi-view images. In this paper, we propose a statistical framework for the active visual 3D reconstruction. We first derive a closed-form
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A Real Time Implementation Of A Bayer Domain Image Deblurring Core For Optical Blur Compensation
In this letter, we present an implementation of deblurring hardware to mitigate blur incurred by optical aberrations in a real-time manner to increase resolution for mobile camera modules. As optical aberrations tend to be variant according to spatial loc
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Trace Norm Generative Adversarial Networks For Sensor Generation And Feature Extraction
Generative Adversarial Networks (GANs) have been shown effective to generate realistic enough sensor data for industrial failure prediction. Compared to computer vision problems, where it is very common to have more than 1000 classes, the number of classe
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A Multichannel Kalman-Based Wiener Filter Approach For Speaker Interference Reduction In Meetings
Recording a meeting and obtaining clean speech signals of each speaker is a challenging task. Even with a multichannel recording, in which all speakers are equipped with a close-talk microphone, speech of an active speaker still couples not only into his
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Simplified Dynamic Sc-Flip Polar Decoding
SC-Flip (SCF) decoding is a low-complexity polar code decoding algorithm alternative to SC-List (SCL) algorithm with small list sizes. To achieve the performance of the SCL algorithm with large list sizes, the Dynamic SC-Flip (DSCF) algorithm was proposed
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Full Reference Video Quality Measures Improvement Using Neural Networks
The accuracy of video quality metrics (VQMs) is an important issue for several applications. In this work, first we observe that the accuracy of several video quality metrics (VQMs) is strongly related to the spatial complexity index (SI) of the source. I
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Non-Uniform Video Time-Lapse Method Based On Motion Scenario And Stabilization Constraint
Time-lapse of user captured video becomes popular in many applications recently, non-uniform sampling and digital video stabilization (VS) are usually two independent steps to keep meaningful contents and provide stabilized output. However, non-uniform sa
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Federated Learning With Quantization Constraints
Traditional deep learning models are trained on centralized servers using labeled sample data collected from edge devices. This data often includes private information, which the users may not be willing to share. Federated learning (FL) is an emerging ap
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Estimating The Degree Of Sleepiness By Integrating Articulatory Feature Knowledge In Raw Waveform Based Cnns
Speech-based degree of sleepiness estimation is an emerging research problem. This paper investigates an end-to-end approach, where given raw waveform as input, a convolutional neural network (CNN) estimates at its output the degree of sleepiness. Within
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Triplet Loss Feature Aggregation For Scalable Hash
The increasing demands of high resolution and quality aggravate the status of heavy burden of cluster storage side and restricted bandwidth resources. Hence, video de-duplication in storage and transmission is becoming an important feature for video cloud
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Sequential Semi-Orthogonal Multi-Level Nmf With Negative Residual Reduction For Network Embedding
Network embedding is intended to produce low-dimensional vector representations of nodes in a network to preserve and extract the latent network structure, which has higher robustness to noise, outliers, and redundant data. Although a recently proposed mu
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Competitive methods for lossless screen content coding are based on modelling of probability distributions. The most effective approach for losslessly compressing images with up to 90000 colours is known as `soft context formation' (SCF). It scans the ima
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Ensemble Network For Ranking Images Based On Visual Appeal
We propose a computational framework for ranking images (group photos) taken at the same event within a short time span. The ranking is expected to correspond with human perception of overall appeal of the images. We hypothesize (and provide evidence thro
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A Framework For The Robust Evaluation Of Sound Event Detection
This work defines a new framework for performance evaluation of polyphonic sound event detection (SED) systems, which overcomes the limitations of the conventional collar-based event decisions, event F-scores and event error rates. The proposed framework
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Compressing Flow Fields With Edge-Aware Homogeneous Diffusion Inpainting
In spite of the fact that efficient compression methods for dense two-dimensional flow fields would be very useful for modern video codecs, hardly any research has been performed in this area so far. Our paper addresses this problem by proposing the first
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Audio Feature Extraction For Vehicle Engine Noise Classification
In this paper we propose a new scheme for vehicle engine noise classification as a more privacy-preserving alternative to classifying vehicles based on video recordings. We establish two scenarios: diesel vs. petrol and heavy goods vehicle vs. personal ca
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Training Code-Switching Language Model With Monolingual Data
A lack of code-switching data complicates the training of code-switching (CS) language models. We propose an approach to train such CS language models on monolingual data only. By constraining and normalizing the output projection matrix in RNN-based lang
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Graph Metric Learning Via Gershgorin Disc Alignment
We propose a fast general projection-free metric learning framework, where the minimization objective $min_{M in cS} Q(M)$ is a convex differentiable function of the metric matrix $M$, and $M$ resides in the set $cS$ of generalized graph Laplacian
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Pose Refinement: Bridging The Gap Between Unsupervised Learning And Geometric Methods For Visual Odometry
Unsupervised Learning based monocular visual odometry (VO) has lately drawn significant attention owing to its potential in label-free leaning ability and robustness to camera parameters and environmental variations. However, due to the lack of pose optim
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Spatial Attentional Bilinear 3D Convolutional Network For Video-Based Autism Spectrum Disorder Detection
Video-based Autism Spectrum Disorder (ASD) detection is a challenge to most video classification networks due to the high degree of similarity between categories. Bilinear pooling is a second-order method, which is widely used in fine-grained visual recog
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Efficient Constrained Encoders Correcting A Single Nucleotide Edit In Dna Storage
A nucleotide substitution is said to occur when a base in {A, T} is substituted for a base in {C, G}, or vice versa. Recent experiment (Heckel et al. 2019) showed that a nucleotide substitution occurs with a significantly higher probability that other sub
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Transformer-Based Text-To-Speech With Weighted Forced Attention
This paper investigates state-of-the-art Transformer- and FastSpeech-based high-fidelity neural text-to-speech (TTS) with full-context label input for pitch accent languages. The aim is to realize faster training than conventional Tacotron-based models. I
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Redundant Convolutional Network With Attention Mechanism For Monaural Speech Enhancement
The redundant convolutional encoder decoder network has proven useful in speech enhancement tasks. It can capture localized time-frequency details of speech signals through both the fully convolutional network structure and feature selection capability re
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Predicting Word Error Rate For Reverberant Speech
Reverberation negatively impacts the performance of automatic speech recognition (ASR). Prior work on quantifying the effect of reverberation has shown that clarity (C50), a parameter that can be estimated from the acoustic impulse response, is correlated
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Multi-Label Consistent Convolutional Transform Learning: Application To Non-Intrusive Load Monitoring
Convolutional transform learning is an unsupervised framework we introduced recently, for feature generation based on learnt convolutions. In this work, we propose a supervised formulation for convolutional transform so as to address the multi-label class
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Unsupervised Multiple Source Localization Using Relative Harmonic Coefficients
This paper presents an unsupervised multi-source localization algorithm using a recently introduced feature called the relative harmonic coefficients. We derive a closed-form expression of the feature and briefly summarize its unique properties. We then e
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Data Augmentation Using Empirical Mode Decomposition On Neural Networks To Classify Impact Noise In Vehicle
In a vehicle, impact noise may occur during steering action due to clearance between parts of steering systems. Via structural path the noise is perceived by the drivers? ears and it can be the cause of a repair campaign. It is importatnt to know where th
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Learning Blind Denoising Network For Noisy Image Deblurring
Noisy image deblurring is to recover the blurry image in the presence of the random noise. The key to this problem is to know the noise level in each iteration. The existing methods manually adjust the regularization parameter for varying noise levels, wh
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Global Traffic State Recovery Via Local Observations With Generative Adversarial Networks
Traffic signal control for a large-scale traffic network is one challenging problem in intelligent transportation systems (ITS). High communication overheads are typically required to achieve the optimal control of the traffic signals in multiple road int
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Low-Rank Approximation Of Matrices Via A Rank-Revealing Factorization With Randomization
Given a matrix A with numerical rank k, the two-sided orthogonal decomposition (TSOD) computes a factorization A = UDV^T , where U and V are unitary, and D is (upper/lower) triangular. TSOD is rank-revealing as the middle factor D reveals the rank of A. T
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In-Network Caching For Hybrid Satellite-Terrestrial Networks Using Deep Reinforcement Learning
Large number of redundant requests in wireless networks have lead to the hybrid satellite-terrestrial networks, where a satellite is used for content placement at edge caches at base stations (BSs), thereby reducing backhaul link usage. In this paper, we
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Multi-Task Center-Of-Pressure Metrics Estimation From Skeleton Using Graph Convolutional Network
Center of pressure (COP) is an important measurement of postural and gait control in human biomechanical studies. A vision-based estimation of COP metrics offers a way to obtain these gold-standard metrics for the detection of balance and gait problems. I
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An Ensemble Based Approach For Generalized Detection Of Spoofing Attacks To Automatic Speaker Recognizers
As automatic speaker recognizer systems become mainstream, voice spoofing attacks are on the rise. Common attack strategies include replay, the use of text-to-speech synthesis, and voice conversion systems. While previously-proposed end-to-end detection f
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Selective Convolutional Network: An Efficient Object Detector With Ignoring Background
It is well known that attention mechanisms can effectively improve the performance of many CNNs including object detectors. Instead of refining feature maps prevalently, we reduce the prohibitive computational complexity by a novel attempt at attention. T
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Gaussian Process Imputation Of Multiple Financial Series
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed. We fo
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Low-Rank Mmwave Mimo Channel Estimation In One-Bit Receivers
Receivers with one-bit analog-to-digital converters (ADCs) are promising for high bandwidth millimeter wave (mmWave) systems as they consume less power than their full resolution counterparts. The extreme quantization in one-bit receivers and the use of l
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Just Noticeable Distortion Based Perceptually Lossless Intra Coding
Perceptual video coding plays a very important role in video codec optimization aiming at removing the perceptual redundancies in video content. In this paper, a just noticeable distortion (JND) guided perceptually lossless coding framework is proposed fo
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Forecasting Sparse Traffic Congestion Patterns Using Message-Passing Rnns
The ability to forecast traffic congestion ahead of time given road conditions has remained a prominent problem in road traffic analysis. In this work, we leverage mobility traces of public transport vehicles tracked by the New York City MTA and formulate
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Attentive Modality Hopping Mechanism For Speech Emotion Recognition
In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system. The traditional approaches tackle this task by independently fusing the knowledge from the various modalities
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Low-Complexity Lstm-Assisted Bit-Flipping Algorithm For Successive Cancellation List Polar Decoder
Polar codes have attracted much attention in the past decade due to their capacity-achieving performance. The higher decoding capacity is required for 5G and beyond 5G (B5G). Although the cyclic redundancy check (CRC)- assisted successive cancellation lis
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Subjective Quality Estimation Using Pesq For Hands-Free Terminals
Previous reports have mentioned the possibility that subjective quality of the echo-suppressed speech signal can be estimated based on perceptual evaluation of speech quality (PESQ), but there are few experimental results. We propose third-party listening
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Prediction Of Voicing And The F0 Contour From Electromagnetic Articulography Data For Articulation-To-Speech Synthesis
Articulation-to-speech synthesis based solely on supraglottal articulation requires some sort of intonation control. This paper examines to what extent the f0 contour of an utterance can be predicted from such supraglottal articulation data. To that end,