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Showing 1701 - 1750 of 1951
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A Time-Frequency Network With Channel Attention And Non-Local Modules For Artificial Bandwidth Extension
Convolution neural networks (CNNs) have been achieving increasing attention for the artificial bandwidth extension (ABE) task recently. However, these methods use the flipped low-frequency phase to reconstruct speech signals, which may lead to the well-kn
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Interpretable Self-Attention Temporal Reasoning For Driving Behavior Understanding
Performing driving behaviors based on causal reasoning is essential to ensure driving safety. In this work, we investigated how state-of-the-art 3D Convolutional Neural Networks (CNNs) perform on classifying driving behaviors based on causal reasoning. We
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Polarizing Front Ends For Robust Cnns
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their ?excessive linearity.? In this paper, we propose a bottom-up strategy for attenuating adversarial perturbations using a nonlinear front end
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A Novel Rank Selection Scheme In Tensor Ring Decomposition Based On Reinforcement Learning For Deep Neural Networks
Tensor decomposition has been proved to be effective for solving many problems in signal processing and machine learning. Recently, tensor decomposition finds its advantage for compressing deep neural networks. In many applications of deep neural networks
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Voice Based Classification Of Patients With Amyotrophic Lateral Sclerosis, Parkinson's Disease And Healthy Controls With Cnn-Lstm Using Transfer Learning
In this paper, we consider 2-class and 3-class classification problems for classifying patients with Amyotrophic Lateral Sclerosis (ALS), Parkinson?s Disease (PD), and Healthy Controls (HC) using a CNN-LSTM network. Classification performance is examined
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Automatic Event Detection Of Rem Sleep Without Atonia From Polysomnography Signals Using Deep Neural Networks
Rapid eye movement (REM) sleep behavior disorder (RBD) is a sleep disorder that features loss of atonia, or REM sleep without atonia (RSWA). RBD and RSWA are early manifestations of degenerative neurological diseases such as Parkinson's and Lewy Body Deme
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Mahalanobis Distance Based Adversarial Network For Anomaly Detection
Anomaly detection techniques are very crucial in multiple business applications, such as cyber security, manufacturing and finance. However, developing anomaly detection methods for high-dimensional data with high speed and good performance is still a cha
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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
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Wind: Wasserstein Inception Distance For Evaluating Generative Adversarial Network Performance
In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Fr?chet Inception Distance (FID),
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Gci Detection From Raw Speech Using A Fully-Convolutional Network
Glottal Closure Instants (GCI) detection consists in automatically detecting temporal locations of most significant excitation of the vocal tract from the speech signal. It is used in many speech analysis and processing applications, and various algorithm
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Oh, Jeez! Or Uh-Huh? A Listener-Aware Backchannel Predictor On Asr Transcriptions
This paper presents our latest investigation on modeling backchannel in conversations. Motivated by a proactive backchanneling theory, we aim at developing a system which acts as a proactive listener by inserting backchannels, such as continuers and asses
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Graphtts: Graph-To-Sequence Modelling In Neural Text-To-Speech
This paper leverages the graph-to-sequence method in neural text-to-speech (GraphTTS), which maps the graph embedding of the input sequence to spectrograms. The graphical inputs consist of node and edge representations constructed from input texts. The en
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Indylstms: Independently Recurrent Lstms
We introduce Independently Recurrent Long Short-term Memory cells: IndyLSTMs. These differ from regular LSTM cells in that the recurrent weights are not modeled as a full matrix, but as a diagonal matrix, i.e. the output and state of each LSTM cell depend
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Bipartite Belief Propagation Polar Decoding With Bit-Flipping
For the scenarios with high throughput requirements, the belief propagation (BP) decoding is one of the most promising decoding strategies for polar codes. By pruning the redundant variable nodes (VNs) and check nodes (CNs) in the original factor graph, t
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High-Resolution Attention Network With Acoustic Segment Model For Acoustic Scene Classification
The spectral information of acoustic scenes is diverse and complex, which poses challenges for acoustic scene tasks. To improve the classification performance, a variety of convolutional neural networks (CNNs) are proposed to extract richer semantic infor
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Low-Complexity Accurate Mmwave Positioning For Single-Antenna Users Based On Angle-Of-Departure And Adaptive Beamforming
The problem of position estimation of a mobile user equipped with a single antenna receiver using downlink transmissions in addressed. The advantages of this setup compared to the classical MIMO and uplink scenarios are analyzed in terms of achievable the
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Analysis Of Acoustic Features For Speech Sound Based Classification Of Asthmatic And Healthy Subjects
Non-speech sounds (cough, wheeze) are typically known to perform better than speech sounds for asthmatic and healthy subject classification. In this work, we use sustained phonations of speech sounds, namely, /A:/, /i:/, /u:/, /eI/, /oU/, /s/, and /z/ fro
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Context And Uncertainty Modeling For Online Speaker Change Detection
Speaker change detection is often addressed as a key component in speaker diarization systems. In this work we focus on online speaker change detection as a standalone task which is required for online closed captioning of broadcast television. Contrary t
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Differentially Modulated Spectrally Efficient Frequency-Division Multiplexing
This letter proposes a differentially modulated non-orthogonal spectrally efficient frequency-division multiplexing (D-SEFDM) architecture, which allows us to dispense with any pilot overhead needed for channel estimation at the receiver, while increasing
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Speech Enhancement Using A Two-Stage Network For An Efficient Boosting Strategy
A novel neural network architecture, called two-stage network (TSN), with a multi-objective learning (MOL) method for an efficient boosting strategy (BS) is proposed for speech enhancement. BS is an ensemble method using multiple base predictions (MBPs) f
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Real-Time Sound Event Detection On The Edge: Porting Vggish On Low-Power Iot Microcontrollers
Internet of Things (IoT) applications typically require a large number of heterogeneous devices to be distributed in the environment, which can generate large amounts of data for wireless transmission, affecting the energy requirements and lifetime of the
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Learning To Transfer Multi-Speaker Emotional Prosody To A Neutral Speaker
Most recent emotional speech synthesizers have been studied with a large training data. These systems require a sufficient number of audios to be recorded with respect to different emotions for each speaker. Acquiring emotional speech is more expensive th
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High-Accuracy Classification Of Attention Deficit Hyperactivity Disorder With L2,1-Norm Linear Discriminant Analysis
Attention Deficit Hyperactivity Disorder (ADHD) is a high incidence of neurobehavioral disease in school-age children. Its neurobiological classification is meaningful for clinicians. The existing ADHD classification methods suffer from two problems, i.e.
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Bilateral Recurrent Network For Single Image Deraining
Single image deraining has been widely studied in recent years. Motivated by residual learning, most deep learning based deraining approaches devote research attention to extracting rain streaks, usually yielding visual artifacts in final deraining images
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Semantic Augmentation Hashing For Zero-Shot Image Retrieval
Hashing technique has been widely applied to large-scale image retrieval due to its efficacy in storage and retrieval. However, due to the explosive growth of multimedia data on the web, existing hashing approaches can hardly achieve satisfactory performa
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Diversity And Sparsity: A New Perspective On Index Tracking
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must a
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Lightweight V-Net For Liver Segmentation
The V-Net based 3D fully convolutional neural networks have been widely used in liver volumetric data segmentation. However, due to the large number of parameters of these networks, 3D FCNs suffer from high computational cost and GPU memory usage. To addr
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Depth Estimation From Single Image Through Multi-Path-Multi-Rate Diverse Feature Extractor
Convolutional neural networks can effectively learn features and predict the depth by considering different scene types. However, previous studies have not accurately predicted the depth in cases wherein the objects or scenes were small and the background
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Discriminant Generative Adversarial Networks With Its Application To Equipment Health Classification
In equipment health classification, machines in normal, degradation and critical stages are classified based on domain experts KPI (Remaining Useful Life). Higher KPI values indicate healthier machines. GANs can be used to generate sensor data for machine
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Position Constraint Loss For Fashion Landmark Estimation
Fashion landmark estimation aims at locating functional key points of clothes, which has wide potential applications in electronic commerce. However, due to the occlusion and weak outline information, landmark estimation occurs outliers and duplicate dete
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Efficient Scene Text Detection With Textual Attention Tower
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multi-oriented text in scene images. The proposed feature fusion
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Snorer Diarisation Based On Deep Neural Network Embeddings
Acoustic analysis of sleep breathing sounds using a smartphone at home provides a much less obtrusive means of screening for sleep-disordered breathing (SDB) than assessment in a sleep clinic. However, application in a home environment is confounded by th
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Robust Full-Fov Depth Estimation In Tele-Wide Camera System
Tele-wide camera system with different Field of View (FoV) lenses becomes very popular in recent mobile devices. Usually it is difficult to obtain full-FoV depth based on traditional stereo-matching methods. Pure Deep Neural Network (DNN) based depth esti
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Learning Task-Based Analog-To-Digital Conversion For Mimo Receivers
Analog-to-digital conversion allows physical signals to be processed using digital hardware. This conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-ampl
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Scene Text Recognition With Temporal Convolutional Encoder
Texts from scene images typically consist of several characters and exhibit a characteristic sequence structure. Existing methods capture the structure with the sequence-to-sequence models by an encoder to have the visual representations and then a decode
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Enhancing The Labelling Of Audio Samples For Automatic Instrument Classification Based On Neural Networks
The polyphonic OpenMIC-2018 dataset is based on weak and incomplete labels. The automatic classification of sound events, based on the VGGish bottleneck layer as proposed before by the AudioSet, implies the classification of only one second at a time, mak
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Robust Symbol-Level Precoding Via Autoencoder-Based Deep Learning
This paper proposes an autoencoder-based symbol-level precoding (SLP) scheme for a massive multiple-input multiple-output (MIMO) system operating in a limited-scattering environment. By recognizing that only imperfect channel state information (CSI) is av
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What Did Your Adversary Believe? Optimal Filtering And Smoothing In Counter-Adversarial Autonomous Systems
We consider fixed-interval smoothing problems for counter-adversarial autonomous systems. An adversary deploys an autonomous filtering and control system that i) measures our current state via a noisy sensor, ii) computes a posterior estimate (belief) and