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Showing 1701 - 1750 of 1951
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Computability Of The Peak Value Of Bandlimited Signals
In this paper we study the peak value problem, i.e., the task of computing the peak value of a bandlimited signal from its samples. The peak value problem is important, for example, in communications, where the peak value of the transmit signal has to be
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Meta-Learning Extractors For Music Source Separation
We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models. This enables efficient parameter-sharing, while still allowing for i
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Unsupervised Variational Bayesian Kalman Filtering For Large-Dimensional Gaussian Systems
This paper considers the unsupervised filtering problem for large-dimensional linear and Gaussian systems, a setup in which the optimal Kalman filter (KF) might not be usable due to the exorbitant computational cost and storage requirements. For this prob
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Cross-Stained Segmentation From Renal Biopsy Images Using Multi-Level Adversarial Learning
Segmentation from renal pathological images is a key step in automatic analyzing the renal histological characteristics. However, the performance of models varies significantly in different types of stained datasets due to the appearance variations. In th
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Multi-Conditioning And Data Augmentation Using Generative Noise Model For Speech Emotion Recognition In Noisy Conditions
Degradation due to additive noise is a significant road block in the real-life deployment of Speech Emotion Recognition (SER) systems. Most of the previous work in this field dealt with the noise degradation either at the signal or at the feature level. I
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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
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Rgb-D Based Multi-Modal Deep Learning For Face Identification
In recent years, the rapid development of depth cameras and wide application scenarios. The depth image information becomes more influential in face identification. In the proposed architecture, we implement the networks in dual CNN paths for color and de
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Fddwnet: A Lightweight Convolutional Neural Network For Real-Time Semantic Segmentation
This paper introduces a lightweight convolutional neural network, called FDDWNet, for real-time accurate semantic segmentation. In contrast to recent advances of lightweightnetworks that prefer to utilize shallow structure, FDDWNet makes an effort to desi
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Performance Study Of A Convolutional Time-Domain Audio Separation Network For Real-Time Speech Denoising
Time-domain audio separation networks based on dilated temporal convolutions have recently been shown to perform very well compared to methods that are based on a time-frequency representation in speech separation tasks, even outperforming an oracle binar
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Source Separation With Weakly Labelled Data: An Approach To Computational Auditory Scene Analysis
Source separation is the task of separating an audio recording into individual sound sources. Source separation is fundamental for computational auditory scene analysis. Previous work on source separation has focused on separating particular sound classes
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A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality And Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In t
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Graph Neural Net Using Analytical Graph Filters And Topology Optimization For Image Denoising
While convolutional neural nets (CNNs) have achieved remarkable performance for a wide range of inverse imaging applications, the filter coefficients are computed in a purely data-driven manner and are not explainable. Inspired by an analytically derived
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Scalable Multilingual Frontend For Tts
This paper describes progress towards making a Neural Text-to-Speech (TTS) Frontend that works for many languages and can be easily extended to new languages. We take a Machine Translation (MT) inspired approach to constructing the frontend, and model bot
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Fast Training Of Deep Neural Networks For Speech Recognition
Training large, deep neural network acoustic models for speech recognition on large datasets takes a long time on a single GPU, motivating research on parallel training algorithms. We present an approach for training a bidirectional LSTM acoustic model on
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Time-Frequency Feature Decomposition Based On Sound Duration For Acoustic Scene Classification
Acoustic scene classification is the task of identifying the type of acoustic environment in which a given audio signal is recorded. The signal is a mixture of sound events with various characteristics. In-depth and focused analysis is needed to find out
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Hierarchical Caching Via Deep Reinforcement Learning
Next generation wireless and wireline networks, including Internet, cellular, and content delivery networks are to serve user file requests proactively. To this aim, by storing anticipated popular contents during off-peak periods, and fetching them to end
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A Variational Bayesian Approach For Multichannel Through-Wall Radar Imaging With Low-Rank And Sparse Priors
This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the radar signals observed along several channels, a joint distribution of the observed data and latent variables is formula
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Corrdrop: Correlation Based Dropout For Convolutional Neural Networks
Convolutional neural networks (CNNs) can be easily over-fitted when they are over-parametered. The popular dropout that drops feature units randomly can't always work well for CNNs, due to the problem of under-dropping. To eliminate this problem, some str
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Complex Trainable Ista For Linear And Nonlinear Inverse Problems
Complex-field signal recovery problems from noisy linear/nonlinear measurements appear in many areas of signal processing and wireless communications. In this paper, we propose a trainable iterative signal recovery algorithm named complex-field TISTA (C-T
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Greedy Hybrid Rate Adaptation In Dynamic Wireless Communication Environment
High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/ne
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On Exponentially Consistency Of Linkage-Based Hierarchical Clustering Algorithm Using Kolmogrov-Smirnov Distance
This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distributions. Th