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Unsupervised Speaker Adaptation Using Attention-Based Speaker Memory For End-To-End Asr
We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a memory block that holds speaker i-vectors extracted from the training data an
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A Dialogical Emotion Decoder For Speech Emotion Recognition In Spoken Dialog
Developing a robust emotion speech recognition (SER) system for human dialog is important in advancing conversational agent design. In this paper, we proposed a novel inference algorithm, a dialogical emotion decoding (DED) algorithm, that treats a dialog
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Reliable And Secure Transmission For Future Networks
This paper introduces a novel physical layer encryption method called randomized reciprocal channel modulation (RRCM) for reliable and secure transmission of information against eavesdropper (Eve) with any number of antennas and any noise level. RRCM make
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Cross-Domain Joint Dictionary Learning For Ecg Reconstruction From Ppg
An emerging research direction considers the inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) to bring about the synergy between the easy measurability of PPG and the rich clinical knowledge of ECG to facilitate preventiv
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Learning With Out-Of-Distribution Data For Audio Classification
In supervised machine learning, the standard assumptions of data and label integrity are not always satisfied due to cost constraints or otherwise. In this paper, we investigate a case of this for classification tasks in which the dataset is corrupted wit
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Node-Asynchronous Spectral Clustering On Directed Graphs
In recent years the convergence behavior of random node asynchronous graph communications have been studied for the case of undirected graphs. This paper extends these results to the case of graphs having arbitrary directed edges possibly with a non-diago
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Lance: Efficient Low-Precision Quantized Winograd Convolution For Neural Networks Based On Graphics Processing Units
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry. In this paper, we propose an efficient low-precision quantized Winograd convolution algorithm, called LANCE, which combines the ad
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Automatic Prediction Of Suicidal Risk In Military Couples Using Multimodal Interaction Cues From Couples Conversations
Suicide is a major societal challenge globally, with a wide range of risk factors, from individual health, psychological and behavioral elements to socio-economic aspects. Military personnel, in particular, are at especially high risk. Crisis resources, w
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Adaptive Elastic Loss Based On Progressive Inter-Class Association For Cervical Histology Image Segmentation
Cervical cancer is one of the most commonly diagnosed cancer types worldwide, while is curable if detected early. However, few computer-aided algorithms have been explored on cervical histology image, which is vital for abnormality assessment. In this pap
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Augmented Grad-Cam: Heat-Maps Super Resolution Through Augmentation
We present Augmented Grad-CAM, a general framework to provide a high-resolution visual explanation of CNN outputs. Our idea is to take advantage of image augmentation to aggregate multiple low-resolution heat-maps -- in our experiments Grad-CAMs -- comput
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On CraméR-Rao Lower Bounds With Random Equality Constraints
Numerous works have shown the versatility of deterministic constrained Cram?r-Rao bound for estimation performance analysis and design of a system of measurement. Indeed, most of factors impacting the asymptotic estimation performance of the parameters of
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Jpeg Steganography With Side Information From The Processing Pipeline
The current art in schemes using deflection criterion such as MiPOD for JPEG steganography is either under-performing or on par with distortion-based schemes. We link this lack of performance to a poor estimation of the variance of the model of the noise
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Stochastic Graph Neural Networks
Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. However, current GNN implementations assume ideal distributed scenarios and ignore link flu
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Large-Context Pointer-Generator Networks For Spoken-To-Written Style Conversion
This paper introduces a spoken-to-written style conversion method that is suitable for handling a series of text such as discourses and conversations. Spoken-to-written style conversion can increase the readability of automatic speech recognition (ASR) ou
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Theoretical Performance Bound Of Uplink Channel Estimation Accuracy In Massive Mimo
In this paper, we present a new performance bound for uplink channel estimation (CE) accuracy in the Massive Multiple Input Multiple Output (MIMO) system. The proposed approach is based on noise power prediction after the CE unit. Our method outperforms t
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Superpixel Segmentation Via Convolutional Neural Networks With Regularized Information Maximization
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We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without any labels by minimizing a proposed o
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Polarization Parameters Estimation With Scalar Sensor Arrays
The scalar sensor array (SSA) is generally assumed insensitive to the polarization of the impinging signals, and only diversely polarized arrays, e.g., the vector (crossed-dipole or tripole) sensor array (VSA), can be used for polarization estimation. How
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Programmable Dataflow Accelerators: A 5G Ofdm Modulation/Demodulation Case Study
Via OFDM technology, FFT and Inverse FFT (IFFT) operators enable the latest 5G radio stan- dards. In these latests standards, the behaviour of FFT and IFFT needs to be flexible, supporting sub-carrier spacings from 15kHz to 480kHz and point sizes of up to
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Scalable Detection And Tracking Of Extended Objects
This paper presents a factor graph formulation and sum-product algorithm (SPA) for scalable detection and tracking of extended objects that generate multiple measurements. The proposed method dynamically introduces newly detected objects into the state sp
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Supervised Online Diarization With Sample Mean Loss For Multi-Domain Data
Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network. In this paper we propose qualitative modifications to the model that significa
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Body Movement Generation For Expressive Violin Performance Applying Neural Networks
Generating body movements based on given music audio recordings is an emerging research topic. This problem remains challenging particularly for string instruments, considering the fact that the relationship between the musical note sequences and the body
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Cs-R-Fcn: Cross-Supervised Learning For Large-Scale Object Detection
Generic object detection is one of the most fundamental problems in computer vision, yet it is difficult to provide all the bounding-box-level annotations aiming at large-scale object detection for thousands of categories. In this paper, we present a nove
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Synthetic Data Generation Through Statistical Explosion: Improving Classification Accuracy Of Coronary Artery Disease Using Ppg
Synthetic data generation has recently emerged as a substitution technique for handling the problem of bulk data needed in training machine learning algorithms. Healthcare, primarily cardiovascular domain is a major area where synthetic physiological data
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Correlated Multi-Armed Bandits With A Latent Random Source
Multi-armed bandit models are widely studied sequential decision-making problems that exemplify the exploration-exploitation trade-off. We study a novel correlated multi-armed bandit model where the rewards obtained from the arms are functions of a common
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A Neural Document Language Modeling Framework For Spoken Document Retrieval
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answering, information retrieval and so on. In the context of natural language pro
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Improving Noise Robust Automatic Speech Recognitionwith Single-Channel Time-Domain Enhancement Network
With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly. However, ASR performance in noisy conditions of single-channel systems remains unsatisfactory. Indeed, most single-channel speech enhance
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Laplace State Space Filter With Exact Inference And Moment Matching
We present a Bayesian filter for state space models with Laplace-distributed observation noise that is robust to heavy-tailed and outlier-ridden univariate time-series data. We analytically derive a closed-form expression of the exact posterior for a Lapl
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Learning Endmember Dynamics In Multitemporal Hyperspectral Data Using A State-Space Model Formulation
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in spite of a trem
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Improved Nearest Neighbor Density-Based Clustering Techniques With Application To Hyperspectral Images
We consider the problem of density-based unsupervised classification in hyperspectral data. Our focus is especially on methods based on K nearest neighbors graph (KNN). In this paper, we propose some improvements of recently published methods in this vein
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On Distributed Stochastic Gradient Algorithms For Global Optimization
The paper considers the problem of network-based computation of global minima in smooth nonconvex optimization problems. It is known that distributed gradient-descent-type algorithms can achieve convergence to the set of global minima by adding slowly dec
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Anomaly Detection For Time Series Using Vae-Lstm Hybrid Model
In this work, we propose a VAE-LSTM hybrid model as an unsupervised approach for anomaly detection in time series. Our model utilizes both a VAE module for forming robust local features over short windows and a LSTM module for estimating the long term cor
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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