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A Generalization Of Principal Component Analysis
Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of principal components
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An Improved Selective Active Noise Control Algorithm Based On Empirical Wavelet Transform
The gradual adaptation and possibility of divergence have been the two main obstacles in the efficient implementation of conventional adaptive active noise control (ANC) to a wider range of applications. Selective ANC (SANC) has been proposed to rapidly r
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Learning A Representation For Cover Song Identification Using Convolutional Neural Network
Cover song identification is a challenging task in the field of Music Information Retrieval (MIR) due to complex musical variations between query tracks and cover versions. Previous works typically utilize hand-crafted features and alignment algorithms fo
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Reduced-Complexity Singular Value Decomposition For Tucker Decomposition: Algorithm And Hardware
Tensors, as the multidimensional generalization of matrices, are naturally suited for representing and processing high dimensional data. To date, tensors have been widely adopted in various data-intensive applications, such as machine learning and big dat
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Counting Dense Objects In Remote Sensing Images
Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing image
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Ednfc-Net: Convolutional Neural Network With Nested Feature Concatenation For Nuclei-Instance Segmentation
Accurate nuclei identification is an important step in diagnosis of several diseases. The problem is complex due to heterogeneity in structure, color, and texture among the different categories of cells. The problem is further complicated due to overlappe
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A Novel Saliency-Driven Oil Tank Detection Method For Synthetic Aperture Radar Images
Synthetic aperture radar (SAR) imaging system plays an important role in earth observation research. This leads to the significance of target detection in SAR image. In this paper, we propose a novel saliency-driven oil tank detection method (SDD) for SAR
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Proximal Distance Algorithm For Nonconvex Qcqp With Beamforming Applications
This paper studies nonconvex quadratically constrained quadratic program (QCQP), which is known to be NP-hard in general. In the past decades, various approximate approaches have been developed to tackle the QCQP, including semidefinite relaxation (SDR),
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Audio Sound Determination Using Feature Space Attention Based Convolution Recurrent Neural Network
The classification framework has been popularly adopted to perform sound event detection. However, the existing neural network based classification based approaches treat each feature dimension equally and the varying influence of feature dimensions has n
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A Return To Dereverberation In The Frequency Domain Using A Joint Learning Approach
Dereverberation is often performed in the time-frequency domain using mostly deep learning approaches. Time-frequency domain processing, however, may not be necessary when reverberation is modeled by the convolution operation. In this paper, we investigat
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Adaptation Of Rnn Transducer With Text-To-Speech Technology For Keyword Spotting
With the advent of recurrent neural network transducer (RNN-T) model, the performance of keyword spotting (KWS) systems has greatly improved. However, the KWS systems, employed for wake-word detection, still rely on the availability of keyword specific tr
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Regression Before Classification For Temporal Action Detection
Action classification combined with location regression is a widely-utilized mechanism in existing temporal action detection methods. However, there exists an inconsistency problem between locations and categories of action instances in this mechanism. Mo
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Resource Management In The Multibeam Noma-Based Satellite Downlink
A beam-free approach to channel allocation in a multi-beam four-color satellite coverage area is taken. Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) are compared as methods to serve users non-necessarily located on the refere
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Iq-Stan: Image Quality Guided Spatio-Temporal Attention Network For License Plate Recognition
License plate recognition (LPR) is one of the essential components in intelligent transportation systems. Although the image processing algorithms for LPR have been extensively studied in the past several years, the recognition performance is still not sa
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A Unified Sequence-To-Sequence Front-End Model For Mandarin Text-To-Speech Synthesis
In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components
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Unsupervised Key Hand Shape Discovery Of Sign Language Videos With Correspondence Sparse Autoencoders
Recognition of sign language is a difficult task which often requires tedious annotations by sign language experts. End-to-end learning attempts that bypass frame level annotations have achieved some success in limited datasets, but it has been shown that
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Self-Supervised Learning For Audio-Visual Speaker Diarization
Speaker diarization, which is to find the speech segments of specific speakers, has been widely used in human-centered applications such as video conferences or human-computer interaction systems. In this paper, we propose a self-supervised audio-video sy
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Balanced Binary Neural Networks With Gated Residual
Binary neural networks have attracted numerous attention in recent years. However, mainly due to the information loss stemming from the biased binarization, how to preserve the accuracy of networks still remains a critical issue. In this paper, we attempt
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Robust Speaker Recognition Using Unsupervised Adversarial Invariance
In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architec
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Text Adaptation For Speaker Verification With Speaker-Text Factorized Embeddings
Text mismatch between pre-collected data, either training data or enrollment data, and the actual test data can significantly hurt text-dependent speaker verification (SV) system performance. Although this problem can be solved by carefully collecting dat
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Multi-View Clustering Via Mixed Embedding Approximation
This paper tackles multi-view clustering via proposing a novel mixed embedding approximation (MEA) method. Formally, we aim to learn a uniform orthogonal embedding based on the orthogonal pre-embeddings of each view. At first, we hope that the uniform emb
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Multilinear Generalized Singular Value Decomposition (Ml-Gsvd) With Application To Coordinated Beamforming In Multi-User Mimo Systems
In this paper, we propose a new Multilinear Generalized Singular Value Decomposition (ML-GSVD) which allows to jointly factorize a set of matrices with one common dimension. The ML-GSVD is an extension of the Generalized Singular Value Decomposition (GSVD
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Wind: Wasserstein Inception Distance For Evaluating Generative Adversarial Network Performance
In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Fr?chet Inception Distance (FID),
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Gci Detection From Raw Speech Using A Fully-Convolutional Network
Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithm
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Oh, Jeez! Or Uh-Huh? A Listener-Aware Backchannel Predictor On Asr Transcriptions
This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a proactive listener by inserting backchannels, such as continuers and asses
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Graphtts: Graph-To-Sequence Modelling In Neural Text-To-Speech
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The en
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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