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Showing 1701 - 1750 of 1951
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Deliberation Model Based Two-Pass End-To-End Speech Recognition
End-to-end (E2E) models have made rapid progress in automatic speech recognition (ASR) and perform competitively relative to conventional models. To further improve the quality, a two-pass model has been proposed to rescore streamed hypotheses using the n
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Regularized Fast Multichannel Nonnegative Matrix Factorization With Ilrma-Based Prior Distribution Of Joint-Diagonalization Process
In this paper, we address a convolutive blind source separation (BSS) problem and propose a new extended framework of FastMNMF by introducing prior information for joint diagonalization of the spatial covariance matrix model. Recently, FastMNMF has been p
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Epi-Neighborhood Distribution Based Light Field Depth Estimation
In this paper, a novel depth estimation algorithm tackling foreground occlusion is proposed based on the neighborhood distribution in the sheared epipolar images (EPIs). First, the EPI is sheared to perform refocusing. Next a series of sheared EPI?s neigh
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Multi Image Depth From Defocus Network With Boundary Cue For Dual Aperture Camera
In this paper, we estimate depth information using two defocused images from dual aperture camera. Recent advances in deep learning techniques have increased the accuracy of depth estimation. Besides, methods of using a defocused image in which an object
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Defense Against Adversarial Attacks On Spoofing Countermeasures Of Asv
Various forefront countermeasure methods for automatic speaker verification (ASV) with considerable performance in anti-spoofing are proposed in the ASVspoof 2019 challenge. However, previous work has shown that countermeasure models are vulnerable to adv
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Improving Device Directedness Classification Of Utterances With Semantic Lexical Features
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature "follow-up" modes that allow users to make additional interactions without the need of a wa
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Comparison Of Glottal Closure Instants Detection Algorithms For Emotional Speech
In production of voiced speech, epochs or glottal closure instants (GCIs) refer to the instants of significant excitation of the vocal tract. Extraction of GCIs is used as a pre-processing stage in many areas of speech technology, such as in prosody modif
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Indoor Altitude Estimation Of Unmanned Aerial Vehicles Using A Bank Of Kalman Filters
Altitude estimation is important for successful control and navigation of unmanned aerial vehicles (UAVs). UAVs do not have indoor access to GPS signals and can only use on-board sensors for reliable estimation of altitude. Unfortunately, most existing na
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Speaker-Aware Training Of Attention-Based End-To-End Speech Recognition Using Neural Speaker Embeddings
In speaker-aware training, a speaker embedding is appended to DNN input features. This allows the DNN to effectively learn representations, which are robust to speaker variability. We apply speaker-aware training to attention-based end-to-end speech recog
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Learning Eating Environments Through Scene Clustering
It is well known that dietary habits have a significant influence on health. While many studies have been conducted to understand this relationship, little is known about the relationship between eating environments and health. Yet researchers and health
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Open Set Video Camera Model Verification
We introduce a new open set video forensics problem called video camera model verification. The video camera model verification task is to determine if two query videos were captured by the same camera model. Importantly, verification must be reliable on
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Solving Non-Convex Non-Differentiable Min-Max Games Using Proximal Gradient Method
Min-max saddle point games appear in a wide range of applications in machine leaning and signal processing. Despite their wide applicability, theoretical studies are mostly limited to the special convex-concave structure. While some recent works generaliz
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Time Difference Of Arrival Estimation From Frequency-Sliding Generalized Cross-Correlations Using Convolutional Neural Networks
The interest in deep learning methods for solving traditional signal processing tasks has been steadily growing in the last years. Time delay estimation (TDE) in adverse scenarios is a challenging problem, where classical approaches based on generalized c
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Metric Representations Of Networks: A Uniqueness Result
In this paper, we consider the problem of projecting networks onto metric spaces. Networks are structures that encode relationships between pairs of elements or nodes. However, these relationships can be independent of each other, and need not be defined
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Phonetic Feedback For Speech Enhancement With And Without Parallel Speech Data
While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to provide high-level feedback. In particular, phonetic feedback is rare in speech
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Overlap-Aware Diarization: Resegmentation Using Neural End-To-End Overlapped Speech Detection
We address the problem of effectively handling overlapping speech in a diarization system. First, we detail a neural Long Short-Term Memory-based architecture for overlap detection. Secondly, detected overlap regions are exploited in conjunction with a fr
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Hybrid Precoding For Secure Transmission In Reflect-Array-Assisted Massive Mimo Systems
Recently, a hybrid analog-digital architecture has been proposed for multiuser MIMO transmission in the millimeter-wave spectrum using reflect-arrays. The architecture exhibits scalability and high energy-efficiency while keeping the transmitter cost-effi
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D-Slam: Diffusion Source Localization And Trajectory Mapping
We consider physical fields induced by a finite number of instantaneous diffusion sources, which we sample using a mobile sensor, along unknown trajectories composed of multiple linear segments. We address the problem of estimating the sources, as well as
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Revealing Hidden Drawings In Leonardo's 'The Virgin Of The Rocks' From Macro X-Ray Fluorescence Scanning Data Through Element Line Localisation
Macro X-Ray Fluorescence (XRF) scanning is an increasingly widely used imaging technique for the non-invasive detection and mapping of chemical elements in Old Master paintings. Existing approaches for XRF signal analysis require varying degrees of expert
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Preconditioning Admm For Fast Decentralized Optimization
In this work, we consider the distributed optimization problem using networked computing machines. Specifically, we are interested in solving this problem using the alternating direction method of multipliers (ADMM) while accounting for edge weights. Exis
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Multi-Resolution Overlapping Stripes Network For Person Re-Identification
This paper addresses the person re-identification (PReID) problem by combining global and local information at multiple feature resolutions with different loss functions. Many previous studies address this problem using either part-based features or globa
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Selective Attention Encoders By Syntactic Graph Convolutional Networks For Document Summarization
Abstractive text summarization is a challenging task, and one need to design a mechanism to effectively extract salient information from the source text and then generate a summary. A parsing process of the source text contains critical syntactic or seman
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A Probabilistic Scheme For Representation Learning With Radial Transform Images
Data representation can facilitate training of deep neural network when limited data is available. We have previously proposed the radial transform sampling method as a data representation technique for training neural networks. In this paper, a probabili
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Joint Blind Calibration And Time-Delay Estimation For Multiband Ranging
In this paper, we focus on the problem of blind joint calibration of multiband transceivers and time-delay (TD) estimation of multipath channels. We show that this problem can be formulated as a particular case of covariance matching. Although this proble
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Addressing Challenges In Building Web-Scale Content Classification Systems
Understanding the semantic meaning of content on the web through the lens of a taxonomy has many practical advantages. However, when building large-scale content classification systems, practitioners are faced with unique challenges involving finding the
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Low-Latency Single Channel Speech Enhancement Using U-Net Convolutional Neural Networks
Single-channel speech enhancement (SE) can be described, in its simplest terms, as learning a transformation from single-channel noisy speech to the clean speech. To do this, we propose a simple but effective U-Net convolutional neural network (CNN) based
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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