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Showing 1851 - 1900 of 1951
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Study Of Formant Modification For Children Asr
The performance of automatic speech recognition systems for children?s speech is known to suffer from the large variation and mismatch in the acoustic and linguistic attributes between children?s and adults? speech. One of the various identified sources o
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Learning-Aided Content Placement In Caching-Enabled Fog Computing Systems Using Thompson Sampling
In this paper, we focus on the problem of online content placement with unknown content popularity in caching-enabled fog computing systems, i.e., how to decide and update cached content on resource-limited edge fog nodes to maximize cache hit rate and mi
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Epoch Extraction From A Speech Signal Using Gammatone Wavelets In A Scattering Network
In speech production, epochs are glottal closure instants where significant energy is released from the lungs. Extracting an epoch accurately is important in speech synthesis, analysis, and pitch oriented studies. The time-varying characteristics of the s
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Super-Resolution With Noisy Measurements: Reconciling Upper And Lower Bounds
This paper considers the problem of lower bounding the mean-squared-error (MSE) of unbiased super-resolution estimates. In literature, only upper bounds on the MSE are available which scale with the so-called super-resolution factor (SRF). However, the up
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A Model-Based Deep Network For Mri Reconstruction Using Approximate Message Passing Algorithm
We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN block
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Achieving The Capacity Of The Dna Storage Channel
Significant advances in biochemical technologies, such as synthesizing and sequencing devices, have made DNA a competitive medium for archival data storage. In this paper we analyze storage systems based on these macromolecules from an information theoret
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Filtering Out Time-Frequency Areas Using Gabor Multipliers
We address the problem of filtering out localized time-frequency components in signals. The problem is formulated as a minimization of a suitable quadratic form, that involves a data fidelity term on the short-time Fourier transform outside the support of
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Cramer-Rao Bound On Doa Estimation Of Finite Bandwidth Signals Using A Moving Sensor
In this paper, we provide a framework for the direction of arrival (DOA) estimation using a moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to s
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Exploiting Vocal Tract Coordination Using Dilated Cnns For Depression Detection In Naturalistic Environments
Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordinati
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Boosted Locality Sensitive Hashing: Discriminative Binary Codes For Source Separation
Speech enhancement tasks have seen significant improvements with the advance of deep learning technology, but with the cost of increased computational complexity. In this study, we propose an adaptive boosting approach to learning locality sensitive hash
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Shadow Removal Of Text Document Images By Estimating Local And Global Background Colors
This paper proposes a simple yet effective method for removing shadows from text document images. Assuming that the document mainly contains texts, our method estimates the global and local background colors using statistical analysis of the whole image a
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Gaussian Processes Over Graphs
Kernel Regression over Graphs (KRG) was recently proposed for predicting graph signals in a supervised learning setting, where the inputs are agnostic to the graph. KRG model predicts targets that are smooth graph signals as over the given graph, given th
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A Comprehensive Framework For 2D-Jnd Extension To 360-Deg Images
Masking effect is one of the most important perceptual properties that could be modeled by estimating an adaptive threshold known as the just noticeable difference (JND) referring to the maximum difference not perceived by the human visual system (HVS). I
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Eigenbeam-Esprit For Doa-Vector Estimation
Several techniques exist to estimate the directions of arrival (DOAs) of sound sources captured with a spherical microphone array. The eigenbeam rotational invariance technique (EB-ESPRIT) uses recurrence relations of spherical harmonics to estimate the D
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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