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Showing 1401 - 1450 of 1951
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A Comprehensive Study Of Residual Cnns For Acoustic Modeling In Asr
Long short-term memory (LSTM) networks are the dominant architecture for large vocabulary continuous speech recognition (LVCSR) acoustic modeling due to their good performance. However, LSTMs are hard to tune and computationally expensive. To build a syst
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Effective Approximation Of Bandlimited Signals And Their Samples
Shannon's sampling theorem is of high importance in signal processing, because it links the continuous-time and discrete-time worlds. For bandlimited signals we can switch from one domain into the other without loosing information. In this paper we analyz
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Enhanced Mixture Population Monte Carlo Via Stochastic Optimization And Markov Chain Monte Carlo Sampling
The population Monte Carlo (PMC) algorithm is a popular adaptive importance sampling (AIS) method used for approximate computation of intractable integrals. Over the years, many advances have been made in the theory and implementation of PMC schemes. The
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A Monte Carlo Search-Based Triplet Sampling Method For Learning Disentangled Representation Of Impulsive Noise On Steering Gear
The classification task of impact noise on vehicle steering system mainly addresses the issue of modeling the transient and impulsive nature. Though various deep learning models including triplet network have been developed, the existing triplet network b
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An Optimal Symmetric Threshold Strategy For Remote Estimation Over The Collision Channel
A wireless sensing system with n sensors, observing independent and identically distributed continuous random variables with a symmetric probability density function, and one non-collocated estimator acting as a fusion center is considered. The sensors tr
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Depth Map Fingerprinting And Splicing Detection
With the ubiquity of social networks, images have become crucial in todays exchange of information. Most of these images are taken by smartphones. For forensic approaches relying on fixed image formation pipelines, the capabilities of smartphones using co
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Signal-Aware Broadband Doa Estimation Using Attention Mechanisms
We refer to direction-of-arrivals (DOAs) estimation of a user-defined subset of directional (desired) sound sources as signal-aware DOA estimation. Source selection, thereby, can be achieved with time-frequency masks to apply attention to TF bins dominate
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Subspace-Based Speech Correlation Vector Estimation For Single-Microphone Multi-Frame Mvdr Filtering
Aiming at exploiting the speech correlation across consecutive time-frames in the short-time Fourier transform domain, the multi-frame minimum variance distortionless response (MFMVDR) filter for single-microphone speech enhancement has been proposed. Thi
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Revisit Of Estimate Sequence For Accelerated Gradient Method
In this paper, we revisit the problem of minimizing a convex function $f(mathbf{x})$ with Lipschitz continuous gradient via accelerated gradient methods (AGM). To do so, we consider the so-called estimate sequence (ES), a useful analysis tool for establi
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Feature Drift Resilient Tracking Of The Carotid Artery Wall Using Unscented Kalman Filtering With Data Fusion
An analysis of the motion of the common carotid artery (CCA) provides effective indicators for cardiovascular diseases. Here, we propose a method for tracking CCA wall motion from a B-mode ultrasound video sequence. An unscented Kalman filter based on a s
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Modeling The Environment In Deep Reinforcement Learning: The Case Of Energy Harvesting Base Stations
In this paper, we focus on the design of energy self-sustainable mobile networks by enabling intelligent energy management that allows the base stations to mostly operate off-grid by using renewable energy. We propose a centralized control algorithm based
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Spherical Video Coding With Geometry And Region Adaptive Transform Domain Temporal Prediction
Many virtual and augmented reality applications depend critically on efficient compression of spherical videos. Current approaches apply a projection geometry to map a spherical video onto the plane(s), wherein a standard codec can be used for compression
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Domain Robust, Fast, And Compact Neural Language Models
Despite advances in neural language modeling, obtaining a good model on a large scale multi-domain dataset still remains a difficult task. We propose training methods for building neural language models for such a task, which are not only domain robust, b
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Generalized Linear Bandits With Safety Constraints
The classical multi-armed bandit is a class of sequential decision making problems where selecting actions incurs costs that are sampled independently from an unknown underlying distribution. Bandit algorithms have many applications in safety critical sys
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A Hybrid Approach For Thermographic Imaging With Deep Learning
We propose a hybrid method for reconstructing thermographic images by combining the recently developed virtual wave concept with deep neural networks. The method can be used to detect defects inside materials in a non-destructive way. We propose two archi
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Learning Differentiable Sparse And Low Rank Networks For Audio-Visual Object Localization
Parsimonious modelling, including sparsity and low rankness, has becomes a cornerstone in modern machine learning and signal processing. However, these modelling techniques have limited capabity to learn from large-scale data, and often require some pre-d
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Deep Learning-Based Beam Alignment In Mmwave Vehicular Networks
Millimeter wave channels exhibit structure that allows beam alignment with fewer channel measurements than exhaustive beam search. From a compressed sensing (CS) perspective, the received channel measurements are usually obtained by multiplying a CS matri
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Deep Casa For Talker-Independent Monaural Speech Separation
Monaural speech separation is the task of separating target speech from interference in single-channel recordings. Although substantial progress has been made recently in deep learning based speech separation, previous studies usually focus on a single ty
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Stabilizing Multi-Agent Deep Reinforcement Learning By Implicitly Estimating Other Agents’ Behaviors
Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it?s power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and
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Learning Connectivity And Higher-Order Interactions In Radial Distribution Grids
To perform any meaningful optimization task, distribution grid operators need to know the topology of their grids. Although power grid topology identification and verification has been recently studied, discovering instantaneous interplay among subsets of
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Classification Of Depth And Surface Edges With Deep Features
Edges in 2D images fall into two categories: depth edges and surface edges, depending on if the edge corresponds to an abrupt change in depth (the distance from the camera). This edge type is an efficient, robust, and effective information in many applica
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Tensor-To-Vector Regression For Multi-Channel Speech Enhancement Based On Tensor-Train Network
We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to
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A Dual-Staged Context Aggregation Method Towards Efficient End-To-End Speech Enhancement
In speech enhancement, an end-to-end deep neural network converts a noisy speech signal to a clean speech directly in time domain without time-frequency transformation or mask estimation. However, aggregating contextual information from a high-resolution
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Resting-State Eeg-Based Biometrics With Signals Features Extracted By Multivariate Empirical Mode Decomposition
EEG-based biometrics has gained great attention in recent years due to its superiority over traditional biometrics in terms of its resistance to circumvention. While there are numerous choices of data acquisition protocol, the present study is carried out
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Training Deep Spiking Neural Networks For Energy-Efficient Neuromorphic Computing
Spiking Neural Networks (SNNs) encode input information temporally using sparse spiking events, which can be harnessed to achieve higher computational efficiency. However, considering the rapid strides in accuracy enabled by Analog Neural Networks (ANNs),
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Speech Emotion Recognition With Local-Global Aware Deep Representation Learning
Convolutional neural networks (CNN) based deep representation learning methods for speech emotion recognition (SER) have demonstrated great success. The basic design of CNN restricts the ability to model only local information well. Capsule network (CapsN
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Multichannel Active Noise Control With Spatial Derivative Constraints To Enlarge The Quiet Zone
Active noise control is an efficient approach in dealing with unwanted acoustic disturbances. However, most of the active noise control algorithms aim to control the signal of the error sensor leading to local noise attenuation only around the error micro
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Speaker Diarization With Session-Level Speaker Embedding Refinement Using Graph Neural Networks
Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for dist
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Korean Singing Voice Synthesis Based On Auto-Regressive Boundary Equilibrium Gan
Singing voice synthesis is a generative task that involves not only multidimensional controls of a singer model such as phonetic modulation by lyrics and pitch control by music score but also expressive elements such as breath sounds and vibrato. Recently
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K-Space Trajectory Design For Reduced Mri Scan Time
The development of compressed sensing (CS) techniques for magnetic resonance imaging (MRI) is enabling a speedup of MRI scanning. To increase the incoherence in the sampling, a random selection of points on the k-space is deployed and a continuous traject
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Intelligent Student Behavior Analysis System For Real Classrooms
In this paper, we design an intelligent student behavior analysis system for recorded classrooms, which automatically detects hand-raising, standing, and sleeping behaviors of students. Detecting these behaviors is quite challenging mainly due to various
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Neural Network Training With Approximate Logarithmic Computations
The high computational complexity associated with training deep neural networks limits online and real-time training on edge devices. This paper proposed an end-to-end training and inference scheme that eliminates multiplications by approximate operations
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Multitaper Spectral Granger Causality With Application To Ssvep
The traditional parametric approach to Granger causality (GC), based on linear vector autoregressive modeling, suffers from difficulties related to the inaccurate modeling of the generative process. These limits can be solved by using non-parametric spect
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Learning To Generate Diverse Questions From Keywords
Diverse text generation has been emerging as an important topic of natural language generation. Traditional studies on question generation mainly investigate how to generate one question based on a given input (one-to-one). In this paper, we focus on a mo
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Synchronous Transformers For End-To-End Speech Recognition
For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchronous problem between the encoding and decoding makes these models difficul
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Multi-Constraint Spectral Co-Design For Colocated Mimo Radar And Mimo Communications
Single waveform design for automotive joint radar-communications (JRC) is being increasingly considered recently, as it addresses the problem of spectrum sharing between the two systems. The paper addresses the challenge of designing a waveform in MIMO-ra
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From Video Game To Real Robot: The Transfer Between Action Spaces
Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training. To address this, transfer learning c
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Spatial-Temporal Feature Aggregation Network For Video Object Detection
Video object detection is a challenging problem in computer vision. In this paper, we propose a novel spatial-temporal feature aggregation network to deal with this issue. Specifically, we present a novel instance-level feature aggregation module as compl
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Steepening Squared Error Function Facilitates Online Adaptation Of Gaussian Scales
We previously proposed a joint learning scheme of Gaussian parameters (scales and centers) and coefficients for online nonlinear estimation. The instantaneous squared error cost in terms of the Gaussian scales, however, tends to have shallow slopes when t
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Information Flow Optimization In Inference Networks
The problem of maximizing the information flow through a sensor network tasked with an inference objective at the fusion center is considered. The sensor nodes take observations, compress and send them to the fusion center through a network of relays. The
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Fractional Fourier Transform Based Qrs Complex Detection In Ecg Signal
By exploiting fractional-Fourier-transform (FrFT), a novel technique for the QRS complex detection is proposed. The application of the FrFT rotates the Electrocardiograph (ECG) signal in the time-frequency plane. We claim this rotation can give simple and
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Singing Voice Conversion With Disentangled Representations Of Singer And Vocal Technique Using Variational Autoencoders
We propose a flexible framework that deals with both singer conversion and singers vocal technique conversion. The proposed model is trained on non-parallel corpora, accommodates many-to-many conversion, and leverages recent advances of variational autoen
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Federated Truth Inference Over Distributed Crowdsourcing Platforms
This work examines the truth inference problem in a distributed crowdsourcing scenario. Labeling tasks are outsourced to workers associated with different platforms, and truth inference is to be performed without sharing the workers' individual responses
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Forecasting Multi-Dimensional Processes Over Graphs
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector
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Principal Angle Detector For Subspace Signal With Structured Unknown Interference
Detecting subspace signals is an important problem in radar and sonar signal processing, hyperspectral image processing, wireless communication, and other fields. Among these problems, a typical scenario is that one needs to detect a signal lying in a giv