Showing 151 - 200 of 1951
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Wirtinger Flow Algorithms For Phase Retrieval From Binary Measurements
We consider the problem of Binary Phase Retrieval, wherein we attempt to recover signals from their quadratic measurements, which are further encoded as +1 or ?1 depending on whether they exceed a threshold or not. Binary encoding is the extreme case of q
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Optimal Design Of Energy-Efficient Cell-Free Massive Mimo: Joint Power Allocation And Load Balancing
A large-scale distributed antenna system that serves the users by coherent joint transmission is called Cell-free Massive MIMO (multiple input multiple output). For a given user set, only a subset of the access points (APs) is likely needed to satisfy the
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Fusion Approaches For Emotion Recognition From Speech Using Acoustic And Text-Based Features
In this paper, we study different approaches for classifying emotions from speech using acoustic and text-based features. We propose to obtain contextualized word embeddings with BERT to represent the information contained in speech transcriptions and sho
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Multi-Branch Learning For Weakly-Labeled Sound Event Detection
There are two sub-tasks implied in the weakly-supervised SED: audio tagging and event boundary detection. Current methods which combine multi-task learning with SED requires annotations both for these two sub-tasks. Since there are only annotations for au
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H-Vectors: Utterance-Level Speaker Embedding Using A Hierarchical Attention Model
In this paper, a hierarchical attention network is proposed to generate utterance-level embeddings (H-vectors) for speaker identification and verification. Since different parts of an utterance may have different contributions to speaker identities, the u
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How Confident Are You? Exploring The Role Of Fillers In The Automatic Prediction Of A Speaker’s Confidence
"Fillers", example "um" in English, have been linked to the "Feeling of Another's Knowing (FOAK)" or the listener's perception of a speaker?s expressed confidence. Yet, in Spoken Language Processing (SLP) they remain unexplored, or overlooked as noise. We
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A Comparative Study Of Estimating Articulatory Movements From Phoneme Sequences And Acoustic Features
Unlike phoneme sequences, movements of speech articulators (lips, tongue, jaw, velum) and the resultant acoustic signal are known to encode not only the linguistic message but also carry para-linguistic information. While several works exist for estimatin
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Distributed Tensor Completion Over Networks
The aim of this paper is to propose a novel distributed strategy for tensor completion, where (partial) data are collected over a network of agents with sparse, but connected, topology. The method hinges on the canonical polyadic decomposition, also known
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Privacy-Preserving Phishing Web Page Classification Via Fully Homomorphic Encryption
This work introduces a fast and lightweight homomorphic-encryption pipeline that enables privacy-preserving machine learning for phishing web page recognition. The primary goals are to use visual features to train an accurate model and to implement an inf
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Spatially Adaptive Intra Mode Pre-Selection For Erp 360 Video Coding
In this work, we propose a spatially adaptive HEVC intra mode pre-selection for equirectangular (ERP) 360 video coding. The proposed technique exploits the spatial characteristics of 360 video in the ERP projection to reduce the complexity of intra predic
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Bba-Net: A Bi-Branch Attention Network For Crowd Counting
In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect re
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Incorporating Written Domain Numeric Grammars Into End-To-End Contextual Speech Recognition Systems For Improved Recognition Of Numeric Sequences
Accurate recognition of numeric sequences is crucial for many contextual speech recognition applications. For example, a user might create a calendar event and be prompted by a virtual assistant for the time, date, and duration of the event. We propose a
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Within-Sample Variability-Invariant Loss For Robust Speaker Recognition Under Noisy Environments
Despite the significant improvements in speaker recognition enabled by deep neural networks, unsatisfactory performance persists under noisy environments. In this paper, we train the speaker embedding network to learn the ``clean'' embedding of the noisy
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Low Rank Activations For Tensor-Based Convolutional Sparse Coding
In this article, we propose to extend the classical Convolutional Sparse Coding model (CSC) to multivariate data by introducing a new tensor CSC model that enforces sparsity and low-rank constraint on the activations. The advantages of this model are thre
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Stock Movement Prediction That Integrates Heterogeneous Data Sources Using Dilated Causal Convolution Networks With Attention
The purpose of this research is to develop a high performing model for stock movement prediction utilizing financial indicators and news data. Until recently, the majority of prediction models have employed only the financial indicators, but they possess
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Exploring Pre-Training With Alignments For Rnn Transducer Based End-To-End Speech Recognition
Recently, the recurrent neural network transducer (RNN-T) architecture has become an emerging trend in end-to-end automatic speech recognition research due to its advantages of being capable for online streaming speech recognition. However, RNN-T training
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Fast Direction-Of-Arrival Estimation Of Multiple Targets Using Deep Learning And Sparse Arrays
In this work, we focus on improving the Direction-of-Arrival (DoA) estimation of multiple targets/sources from a small number of snapshots. Estimation via the sample covariance matrix is known to perform poorly, since the true manifold structure is not re
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Cra: A Generic Compression Ratio Adapter For End-To-End Data-Driven Image Compressive Sensing Reconstruction Frameworks
End-to-end data-driven image compressive sensing reconstruction (EDCSR) frameworks achieve state-of-the-art reconstruction performance in terms of reconstruction speed and accuracy. However, due to their end-to-end nature, existing EDCSR frameworks can no
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Slow-Time Mimo-Fmcw Automotive Radar Detection With Imperfect Waveform Separation
This paper considers object detection in the case of imperfect waveform separation, in the context of automotive radars that employ a slow-time MIMO-FMCW signaling scheme. We develop an explicit signal model that accounts for waveform separation residuals
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Accurate 6D Object Pose Estimation By Pose Conditioned Mesh Reconstruction
Current 6D object pose estimation methods consist of Deep Convolutional Neural Networks fully optimized for a single object but with its architecture standardized among objects with different shapes. In contrast to previous works, we explicitly exploit ea
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Low-Frequency Compensated Synthetic Impulse Responses For Improved Far-Field Speech Recognition
We propose a method for generating low-frequency compensated synthetic impulse responses that improve the performance of far-field speech recognition systems trained on artificially augmented datasets. We design linear-phase filters that adapt the simulat
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Channel Charting: An Euclidean Distance Matrix Completion Perspective
Channel charting (CC) is an emerging machine learning framework that aims at learning lower-dimensional representations of the radio geometry from collected channel state information (CSI) in an area of interest, such that spatial relations of the represe
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Libri-Adapt: A New Speech Dataset For Unsupervised Domain Adaptation
This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models. Built on top of the LibriSpeech corpus, Libri-Adapt contains 7200 hours of English speech recorded on mobile and embedded-sc
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Label Reuse For Efficient Semi-Supervised Learning
In this paper, we propose a new learning strategy for semi-supervised deep learning algorithms, called label reuse, aiming to significantly reduce the expensive computational cost of pseudo label generation and the like for each unlabeled training instanc
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End-To-End Non-Negative Autoencoders For Sound Source Separation
Discriminative models for source separation have recently been shown to produce impressive results. However, when operating on sources outside of the training set, these models can not perform as well and are cumbersome to update. Classical methods like N
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Retrieving Vocal-Tract Resonance And Anti-Resonance From High-Pitched Vowels Using A Rahmonic Subtraction Technique
Vocal tract resonances give rise to core spectral information of speech signals. Linear prediction and cepstral methods are widely used for this purpose. However, both approaches are prone to fail as the fundamental frequency (F0) rises. In this study, a
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Passive Intelligent Surface Assisted Mimo Powered Sustainable Iot
Lately, Passive Intelligent Surfaces (PIS) are being recognized to play an important role in meeting the timely demand of low-cost green sustainable Internet of Things (IoT). In this paper, we focus on maximizing the sum received power among the energy ha
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End-To-End Code-Switching Tts With Cross-Lingual Language Model
Code-switching text-to-speech (TTS) aims to enable a system to speak two languages with a single voice and in the same utterance. In this paper, we propose to incorporate cross-lingual word embedding into an end-to-end TTS system, to improve the voice ren
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Lai-Net: Local-Ancestry Inference With Neural Networks
Local-ancestry inference (LAI), also referred to as ancestry deconvolution, provides high-resolution ancestry estimation along the human genome. In both research and industry, LAI is emerging as a critical step in personalized DNA sequence analysis with a
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Atomic Norm Based Localization Of Far-Field And Near-Field Signals With Generalized Symmetric Arrays
Most localization methods for mixed far-field (FF) and nearfield (NF) sources are based on uniform linear array (ULA) rather than sparse linear array (SLA). In this paper, we propose a localization method for mixed FF and NF sources based on the generaliz
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Training Keyword Spotters With Limited And Synthesized Speech Data
With the rise of low power speech-enabled devices, there is a growing demand to quickly produce models for recognizing arbitrary sets of keywords. As with many machine learning tasks, one of the most challenging parts in the model creation process is obta
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Classify And Explain: An Interpretable Convolutional Neural Network For Lung Cancer Diagnosis
The deep network-based computer-aided diagnosis systems have encountered many difficulties in practical applications because of its "black box" feature. The crux of the problem is that these models should be explainable ? the model should provide doctors
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Foreground Signature Extraction For An Intimate Mixing Model In Hyperspectral Image Classification
The hyperspectral unmixing problem arises in remote sensing, chemometrics, and biomedical engineering applications. The spectral signature of a single pixel in a hyperspectral cube can be represented as a non-negative combination of non-negative signature
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A Hierarchical Tracker For Multi-Domain Dialogue State Tracking
The goal of Dialogue State Tracking (DST) is to estimate the current dialogue state given all the preceding conversation. Due to the increased number of state candidates, data sparsity problem is still a major hurdle for multi-domain DST. Existing methods
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Frequency Diverse Array Radar: A Closed-Form Solution To Design Weights For Desired Beampattern
In contrast to phased-array radar, frequency-diverse-array (FDA) radar transmits signals of linearly increasing frequencies across the array. As a consequence, the beampattern of an FDA radar becomes range, angle, and time dependent, which is different fr
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M-Estimators Of Scatter With Eigenvalue Shrinkage
A popular regularized (shrinkage) covariance estimator is the shrinkage sample covariance matrix (SCM) which shares the same set of eigenvectors as the SCM but shrinks its eigenvalues toward its grand mean. In this paper, a more general approach is consid
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Speech Intelligibility Enhancement By Equalization For In-Car Applications
In this paper, we propose a speech intelligibility enhancement method for typical in-car applications in noisy environments. While traditional speech enhancement algorithms aim at increasing the Signal to Noise Ratio (SNR), the goal here is to increase in
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Content Vs Context: How About "walking Hand-In-Hand" For Image Clustering?
Image clustering has been one of the most important issues in the field of pattern recognition. However, most of existing methods only focus on utilizing either content or context information of images, failing to consider both of them. In fact, the power
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Weakly Supervised Semantic Segmentation For Remote Sensing Hyperspectral Imaging
This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on trainin
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On End-To-End Multi-Channel Time Domain Speech Separation In Reverberant Environments
This paper introduces a new method for multi-channel time domain speech separation in reverberant environments. A fully-convolutional neural network structure has been used to directly separate speech from multiple microphone recordings, with no need of c
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Portfolio Cuts: A Graph-Theoretic Framework To Diversification
Investment returns naturally reside on irregular domains, however, standard multivariate portfolio optimization methods are agnostic to data structure. To this end, we investigate ways for domain knowledge to be conveniently incorporated into the analysis
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Hi-Mia : A Far-Field Text-Dependent Speaker Verification Database And The Baselines
This paper presents a large far-field text-dependent speaker verification database named HI-MIA. We aim to meet the data requirement for far-field microphone array based speaker verification since most of the publicly available databases are single channe
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Characterization Of A Snapshot Fourier Transform Imagingspectrometer Based On An Array Of Fabry-Perot Interferometers
This study focuses on a novel snapshot Fourier Transform imaging spectrometer based on an array of Fabry-Perot interferometers. This device fully relies on signal processing in order to provide intelligible outputs and thus requires a precise characterisa
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Maximally Energy-Concentrated Differential Window For Phase-Aware Signal Processing Using Instantaneous Frequency
The short-time Fourier transform (STFT) is widely employed in nonstationary signal analysis, whose property depends on window functions. Instantaneous frequency in STFT, the time-derivative of phase, is recently applied to many applications including spec
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Deep Multi-Region Hashing
Hashing has been widely used for large-scale approximate nearest neighbors retrieval own to its high efficiency. In the existing hashing methods, deep supervised hashing methods have achieved the best performance by utilizing the semantic labels on data w
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Beam Elimination Based On Sequentially Estimated A Posteriori Probabilities Of Winning
A robust and adaptive variable length beam selection strategy based on M-ary sequential competition was proposed in [1]. It was enhanced by the elimination of inauspicious beams during the ongoing competition to improve the efficiency and speed of the tra