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Fast Optical System Identification By Numerical Interferometry
We propose a numerical interferometry method for identification of optical multiply-scattering systems when only intensity can be measured. Our method simplifies the calibration of optical transmission matrices from a quadratic to a linear inverse problem
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A Deep Learning Architecture For Epileptic Seizure Classification Based On Object And Action Recognition
Epilepsy affects approximately 1% of the world’s population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to c
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Hybrid Neural-Parametric F0 Model For Singing Synthesis
We propose a novel hybrid neural-parametric fundamental frequency generation model for singing voice synthesis. A recurrent neural network predicts the parameters of a flexible parametric F0 model, conditioned on a given input score. Rather than trying to
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Automatic Classification Of Volumes Of Water Using Swallow Sounds From Cervical Auscultation
The signatures of swallowing vary depending on the volume of bolus swallowed. Among existing instrumental methods, cervical auscultation (CA) captures the acoustic signatures of the swallow sound. Although many features present in the literature can chara
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Anomalous Sound Detection Based On Interpolation Deep Neural Network
As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoenc
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Localized Linear Regression In Networked Data
The network Lasso (nLasso) has been proposed recently as an efficient learning algorithm for massive networked data sets (big data over networks). It extends the well-known least absolute shrinkage and selection operator (Lasso) from learning sparse (gene
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Connections Between Spectral Properties Of Asymptotic Mappings And Solutions To Wireless Network Problems
We establish connections between asymptotic functions and properties of solutions to problems in wireless networks. We start by introducing self-mappings (called asymptotic mappings) constructed with asymptotic functions, and we show that their spectral p
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Super-Resolution Via Image-Adapted Denoising Cnns: Incorporating External And Internal Learning
While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mis
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Learning Based Reconfigurable Wideband Non-Contiguous Spectrum Characterization For 5G Applications
Introduction of spectrum sharing in 3GPP Release 17 demands base-stations (BS) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Since, multiple-antenna and beam-forming
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S-Dod-Cnn: Doubly Injecting Spatially-Preserved Object Information For Event Recognition
We present a novel event recognition approach called Spatially-preserved Doubly-injected Object Detection CNN (S-DOD-CNN), which incorporates the spatially preserved object detection information in both a direct and an indirect way. Indirect injection is
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Robust Online Matrix Completion With Gaussian Mixture Model
In this paper, we study the problem of online matrix completion (MC) aiming to achieve robustness to the variations in both low-rank subspace and noises. In contrast to existing methods, we progressively fit a specific Gaussian Mixture Model (GMM) for noi
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Ernet Family: Hardware-Oriented Cnn Models For Computational Imaging Using Block-Based Inference
Convolutional neural networks (CNNs) demand huge DRAM bandwidth for computational imaging tasks, and block-based processing has recently been applied to greatly reduce the bandwidth. However, the induced additional computation for feature recomputing or t
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Encoder-Recurrent Decoder Network For Single Image Dehazing
This paper develops a deep learning model, called Encoder-Recurrent Decoder Network (ERDN), which recovers the clear image from a degrade hazy image without using the atmospheric scattering model. The proposed model consists of two key components- an enco
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Multi-View Bayesian Generative Model For Multi-Subject Fmri Data On Brain Decoding Of Viewed Image Categories
Brain decoding studies have demonstrated that viewed image categories can be estimated from human functional magnetic resonance imaging (fMRI) activity. However, there are still limitations with the estimation performance because of the characteristics of
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Learning A Generic Adaptive Wavelet Shrinkage Function For Denoising
The rise of machine learning in image processing has created a gap between trainable data-driven and classical model-driven approaches: While learning-based models often show superior performance, classical ones are often more transparent. To reduce this
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Electric Analog Circuit Design With Hypernetworks And A Differential Simulator
The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects th
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Using Separate Losses For Speech And Noise In Mask-Based Speech Enhancement
Estimating time-frequency domain masks for speech enhancement using deep learning approaches has recently become a popular field in research. In this paper, we propose a novel components loss (CL) for the training of neural networks for speech enhancement
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Efficient Decoupled Neural Architecture Search By Structure And Operation Sampling
We propose a novel neural architecture search algorithm via reinforcement learning by decoupling structure and operation search. Our approach samples candidate models from the multinomial distribution over the policy vectors. The proposed technique improv
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Spatially Guided Independent Vector Analysis
We present a Maximum A Posteriori (MAP) derivation of the Independent Vector Analysis (IVA) algorithm for blind source separation incorporating an additional spatial prior over over the demixing matrices. In this way, the outer permutation ambiguity of IV
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Jointly Optimal Dereverberation And Beamforming
We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority over the widely used cascade of a Weighted Prediction Error (WPE) dereverb
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Spherical Large Intelligent Surfaces
As an emerging technology and evolution that goes beyond massive multi-input multi-output (MIMO), large intelligent surface (LIS) has gained much interest. LIS acts as an electromagnetic surface that can transmit, redirect, and receive radiating signals a
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Estimating Structural Missing Values Via Low-Tubal-Rank Tensor Completion
The recently proposed Tensor Nuclear Norm (TNN) minimization has been widely used for tensor completion. However, previous works didn't consider the structural difference between the observed data and missing data, which widely exists in many applications
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Metric Learning With Background Noise Class For Few-Shot Detection Of Rare Sound Events
Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning. However, such systems have only been applied to chunks of sounds for classification or v
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Improving Speech Recognition Using Consistent Predictions On Synthesized Speech
Speech synthesis has advanced to the point of being close to indistinguishable from human speech. However, efforts to train speech recognition systems on synthesized utterances have not been able to show that synthesized data can be effectively used to au
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Joint Frequency Domain Channel Estimation And Equalization Based On Expectation Propagation For Single Carrier Transmissions
In this paper, a novel category of expectation propagation (EP) based frequency domain (FD) semi-blind receivers are proposed for single-carrier block transmissions. A recently proposed EP-based framework for deriving double-loop turbo detectors is extend
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Preservation Of Anomalous Subgroups On Variational Autoencoder Transformed Data
We investigate the effect of variational autoencoder (VAE) based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group?s
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Fine-Grained Giant Panda Identification
The image-based fine-grained identification of individual giant pandas (Ailuropoda melanoleuca) is an emerging technology, and it is extraordinarily challenging due to the extremely subtle visual differences between individual giant pandas and limited ann
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Addressing The Polysemy Problem In Language Modeling With Attentional Multi-Sense Embeddings
Neural network language models have gained considerable popularity due to their promising performance. Distributed word embeddings are utilized to represent semantic information. However, each word is associated with a single vector in the embedding layer
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A Data Efficient End-To-End Spoken Language Understanding Architecture
Many end-to-end architectures have been recently proposed for spoken language understanding (SLU) and semantic parsing. Based on a large amount of data, those models learn jointly acoustic and linguistic-sequential features. While those architectures give
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Efficient Algorithm To Implement Sliding Singular Spectrum Analysis With Application To Biomedical Signal Denoising
Previous work [1] has shown that Singular Spectrum Analysis (SSA) can be particularly effective at noise removal or signal separation in the case of single channel mixtures. The work presented here shows how the sliding or updating algorithm, which perfor
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Modeling Plate And Spring Reverberation Using A Dsp-Informed Deep Neural Network
Plate and spring reverberators are electromechanical systems first used and researched as means to substitute real room reverberation. Currently, they are often used in music production for aesthetic reasons due to their particular sonic characteristics.
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Channel Adversarial Training For Speaker Verification And Diarization
Previous work has encouraged domain-invariance in deep speaker embedding by adversarially classifying the dataset or labelled environment to which the generated features belong. We propose a training strategy which aims to produce features that are invari
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Specaugment On Large Scale Datasets
Recently, SpecAugment, an augmentation scheme for automatic speech recognition that acts directly on the spectrogram of input utterances, has shown to be highly effective in enhancing the performance of end-to-end networks on public datasets. In this pape
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Crnn-Ctc Based Mandarin Keywords Spotting
Deep learning based approaches have greatly improved the performance of spoken keyword spotting (KWS). However, KWS of different languages should have their own corresponding modeling units to optimize the performance. In this paper, we propose an end-to-
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Cross-Domain Adaptation For Biometric Identification Using Photoplethysmogram
The adoption of biomedical signals such as photoplethysmogram (PPG) and electrocardiogram (ECG) for health parameter estimation on wearable devices is growing in tandem with the increase of attention in mobile healthcare. In our work, we use PPG signals e
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A Zeroth-Order Learning Algorithm For Ergodic Optimization Of Wireless Systems With No Models And No Gradients
Optimal resource allocation in real-world wireless systems is rather challenging, not only due to the unavailability of accurate statistical channel models, but also because expressions of maximal or achievable information rates are most often unknown, or
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Video Question Generation Via Semantic Rich Cross-Modal Self-Attention Networks Learning
We introduce a novel task, Video Question Generation (Video QG). A Video QG model automatically generates questions given a video clip and its corresponding dialogues. Video QG requires a range of skills -- sentence comprehension, temporal relation, the i
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Learning The Helix Topology Of Musical Pitch
To explain the consonance of octaves, music psychologists represent pitch as a helix where azimuth and axial coordinate correspond to pitch class and pitch height respectively. This article addresses the problem of discovering this helical structure from
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On The Degrees Of Freedom In Total Variation Minimization
In the theory of linear model, the degrees of freedom (DOF) of an estimator plays a pivotal role in the risk estimation, as it quantifies the complexity of a statistical modeling procedure. Considering the total-variation (TV) regularization, we in this p
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Rethinking Temporal-Related Sample For Human Action Recognition
Temporal-related samples always have huge intra-class appearance variation, on which lots of existing action recognition algorithms have poor performance. In this paper, our motivation is to address this issue by utilizing temporal information more effect
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Large-Scale Unsupervised Pre-Training For End-To-End Spoken Language Understanding
End-to-end Spoken Language Understanding (SLU) is proposed to infer the semantic meaning directly from audio features without intermediate text representation. In this paper, we explore unsupervised pre-training for End-to-end SLU models by learning repre
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A New Sampling Scheme For Distributed Blind Spectrum Sensing Using Energy Detectors
In this paper, we study the problem of blind spectrum sensing by exploring signal sampling at each cognitive radio (CR) in a distributed cognitive radio network. Specifically, a new cooperative sampling scheme is proposed to deal with the challenge of unk
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Formulating Divergence Framework For Multiclass Motor Imagery Eeg Brain Computer Interface
The ubiquitous presence of non-stationarities in the EEG signals significantly perturb the feature distribution thus deteriorating the performance of Brain Computer Interface. In this work, a novel method is proposed based on Joint Approximate Diagonaliza
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Balancing Rates And Variance Via Adaptive Batch-Sizes In First-Order Stochastic Optimization
Stochastic gradient descent is a canonical tool for addressing stochastic optimization problems, and forms the bedrock of modern machine learning and statistics. In this work, we seek to balance the fact that attenuating step-sizes is required for exact a
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Sequence-To-Sequence Labanotation Generation Based On Motion Capture Data
Labanotation is an important notation system for recording dances. Automatically generating Labanotation scores from motion capture data has attracted more interest in recent years. Current methods usually focus on individual movement segments and generat
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Energy Disaggregation Using Fractional Calculus
Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by one smartmeter. In this article we introduce the use of fractional calculus in the Non-Intr
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An Empirical Bayes Approach To Partially Labeled And Shuffled Data Sets
This work outlines a method for an application of empirical Bayes in the setting of semi-supervised learning. That is, we consider a scenario in which the training set is partially or entirely unlabeled. In addition to the missing labels, we also consider
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Resilient Distributed Recovery Of Large Fields
This paper studies the resilient distributed recovery of large fields under measurement attacks, by a team of agents, where each measures a small subset of the components of a large spatially distributed field. An adversary corrupts some of the measuremen
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Hybrid Active Contour Driven By Double-Weighted Signed Pressure Force For Image Segmentation
In this paper, we proposed a novel hybrid active contour driven by double-weighted signed pressure force method for image segmentation. First, the Legendre polynomials and global information are integrated into the signed pressure force (SPF) function and
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Dilated Convolutional Neural Networks For Panoramic Image Saliency Prediction
Saliency prediction is an important way to understand human?s behavior and has a wide range of applications. Although lots of algorithms have been designed to predict saliency for planar images, there are few works for 360? images. In this paper, we propo