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A Sparse Linear Array Approach In Automotive Radars Using Matrix Completion
We consider an automotive radar using a sparse linear array (SLA) in the context of multi-input multi-output (MIMO) radar. The key problem in SLA is the selection of the locations of the array elements so that the peak sidelobe level of the virtual SLA be
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End-End Speech-To-Text Translation With Modality Agnostic Meta-Learning
Collecting large amounts of data to train end-to-end Speech Translation (ST) models is more difficult compared to the ASR and MT tasks. Previous studies have proposed the use of transfer learning approaches to overcome the above difficulty. These approach
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Distributed Verification Of Belief Precisions Convergence In Gaussian Belief Propagation
Gaussian belief propagation (BP) finds extensive applications in signal processing but it is not guaranteed to converge in loopy graphs. In order to determine whether Gaussian BP would converge, one could directly use the classical convergence conditions
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Array-Geometry-Aware Spatial Active Noise Control Based On Direction-Of-Arrival Weighting
Active noise control (ANC) over a sizeable space ideally requires uniformly distributed sensors and secondary sources, which limits the feasibility of practically realizing such systems. In this paper, we propose a direction of arrival (DOA) weighting alg
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Static Visual Spatial Priors For Doa Estimation
As we interact with the world, for example when we communicate with our colleagues in a large open space or meeting room, we continuously analyse the surrounding environment and, in particular, localise and recognise acoustic events. While we largely take
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Controlling The Perceived Sound Quality For Dialogue Enhancement With Deep Learning
Speech enhancement attenuates interfering sounds in speech signals but may introduce artifacts that perceivably deteriorate the output signal. We propose a method for controlling the trade-off between the attenuation of the interfering background signal a
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A Self-Attentive Emotion Recognition Network
Attention networks constitute the state-of-the-art paradigm for capturing long temporal dynamics. This paper examines the efficacy of this paradigm in the challenging task of emotion recognition in dyadic conversations. In this work, we introduce a novel
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Reinforced Depth-Aware Deep Learning For Single Image Dehazing
Image dehazing continues to be one of the most challenging inverse problems. However, most deep learning-based methods usually design a regression network as a black-box tool to either estimate the dehazed image and/or the physical parameters in the haze
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Arnet:Attention-Based Refinement Network For Few-Shot Semantic Segmentation
Semantic segmentation is a challenging task for computer vision which aims to classify the objects from the pixel level. Previous methods based on deep learning have made some progress but the labeling work is very time-consuming. Few-shot semantic segmen
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Flow-Tts: A Non-Autoregressive Network For Text To Speech Based On Flow
In this work, we propose Flow-TTS, a non-autoregressive end-to-end neural TTS model based on generative flow. Unlike other non-autoregressive models, Flow-TTS can achieve high-quality speech generation by using a single feed-forward network. To our knowle
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Temporal Coding In Spiking Neural Networks With Alpha Synaptic Function
We propose a spiking neural network model that encodes information in the relative timing of individual neuron spikes and performs classification using the first output neuron to spike. This temporal coding scheme allows the supervised training of the net
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A Low-Resolution Adc Proof-Of-Concept Development For A Fully-Digital Millimeter-Wave Joint Communication-Radar
A fully-digital mmWave wideband JCR places difficult demands of power consumption and hardware complexity on the receivers' analog-to-digital converters (ADCs). To address these concerns, we present a low-complexity proof-of-concept (PoC) development for
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Speaker extraction aims to separate a target speaker from multiple voices which is useful for applications, e.g. teleconference. In many practical cases, it has an opportunity to get a piece voice of the target speaker in advance, which provides useful in
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Joint Sparse Recovery Using Deep Unfolding With Application To Massive Random Access
We propose a learning-based joint sparse recovery method for the multiple measurement vector (MMV) problem using deep unfolding. We unfold an iterative alternating direction method of multipliers (ADM) algorithm for MMV joint sparse recovery algorithm int
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Wideband Direction Of Arrival Estimation With Sparse Linear Arrays
This paper concerns wideband direction of arrival (DoA) estimation with sparse linear arrays (SLAs). We rely on the assumption that the power spectrum of the wideband sources is the same up to a scaling factor, which could in theory allow us to resolve no
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Graphical Evolutionary Game Theoretic Analysis Of Super Users In Information Diffusion
In social networks, to better understand the avalanche of information flow over networks and to investigate its impact on economy and our social life, it is of crucial importance to model and analyze the information diffusion process. To address the exist
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Quickest Change Detection In Anonymous Heterogeneous Sensor Networks
The problem of quickest change detection (QCD) in anonymous heterogeneous sensor networks is studied. There are $n$ heterogeneous sensors and a fusion center. The sensors are clustered into $K$ groups, and different groups follow different data generating
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Kalm: Key Area Localization Mechanism For Abnormality Detection In Musculoskeletal Radiographs
Recently abnormality detection in musculoskeletal radiographs has attracted many attentions. For abnormality detection, it is crucial to locate the most important area in the musculoskeletal radiographs. To achieve this goal, we propose a key area localiz
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A Fast Reduced-Rank Sound Zone Control Algorithm Using The Conjugate Gradient Method
Sound zone control enables different users to enjoy different audio contents in the same acoustic environment. Generalized eigenvalue decomposition (GEVD)-based methods allow us to control the trade-off between the acoustic contrast (AC) and signal distor
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A Prototypical Triplet Loss For Cover Detection
Automatic cover detection -- the task of finding in a audio dataset all covers of a query track -- has long been a challenging theoretical problem in MIR community. It also became a practical need for music composers societies requiring to detect automati
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Performance Bounds For Displaced Sensor Automotive Radar Imaging
In automotive radar imaging, displaced sensors offer improvement in localization accuracy by jointly processing the data acquired from multiple radar units, each of which may have limited individual resources. In this paper, we derive performance bounds o
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Analyzing Asr Pretraining For Low-Resource Speech-To-Text Translation
Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pretraining an end-to-end model on automatic speech recognition (ASR) data from a high-resource language. However, it is not clea
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Stability Of Graph Neural Networks To Relative Perturbations
Graph neural networks (GNNs), consisting of a cascade of layers applying a graph convolution followed by a pointwise nonlinearity, have become a powerful architecture to process signals supported on graphs. Graph convolutions (and thus, GNNs), rely heavil
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A New Multihypothesis Prediction Scheme For Compressed Video Sensing Reconstruction
For multihypothesis-based compressed video sensing schemes, the low accuracy of weight prediction and degradation of recovery quality for high-motion videos are open challenges. To solve this problem, this paper proposes a new multihypothesis prediction s
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Mspec-Net : Multi-Domain Speech Conversion Network
In this paper, we present a multi-domain speech conversion technique by proposing a Multi-domain Speech Conversion Network (MSpeC-Net) architecture for solving the less-explored area of Non-Audible Murmur-to-SPeeCH (NAM2-SPCH) conversion. The murmur produ
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Full-Sum Decoding For Hybrid Hmm Based Speech Recognition Using Lstm Language Model
In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should be applied in decoding. This motivate
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Using Personalized Speech Synthesis And Neural Language Generator For Rapid Speaker Adaptation
We propose to use the personalized speech synthesis and the neural language generator to synthesize content relevant personalized speech for rapid speaker adaptation. It has two distinct aspects: First, it relieves the general data sparsity issue in rapid
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Signal Sensing And Reconstruction Paradigms For A Novel Multi-Source Static Computed Tomography System
Conventional Computed Tomography (CT) systems use a single X-ray source and an arc of detectors mounted on a rotating gantry to acquire a set of projection data. Novel CT systems are now being pioneered in which a complete ring of distributed X-ray source
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Computation Of "Best" Interpolants In The Lp Sense
We study a variant of the interpolation problem where the continuously defined solution is regularized by minimizing the Lp-norm of its second-order derivative. For this continuous-domain problem, we propose an exact discretization scheme that restricts t
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Fast Intent Classification For Spoken Language Understanding Systems
Spoken Language Understanding (SLU) systems consist of several machine learning components operating together (e.g. intent classification, named entity resolution and recognition). Deep learning models have obtained state of the art results on several of
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Exploiting Channel Locality For Adaptive Massive Mimo Signal Detection
We propose MMNet, a deep learning MIMO detection scheme that significantly outperforms existing approaches on realistic channels with the same or lower computational complexity. MMNet?s design builds on the theory of iterative soft-thresholding algorithms
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A Hybrid Model For Bipolar Disorder Classification From Visual Information
Bipolar Disorder (BD) is one of the most prevalent mental illnesses in the world. It has a negative impact on people?s social and personal functions. The principal indicator of BD is the extreme swing in the mood ranging from manic to depressive states. T
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Densely Connected Neural Network With Dilated Convolutions For Real-Time Speech Enhancement In The Time Domain
In this work, we propose a fully convolutional neural network for real-time speech enhancement in the time domain. The proposed network is an encoder-decoder based architecture with skip connections. The layers in the encoder and the decoder are followed
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Person Identification Using Deep Convolutional Neural Networks On Short-Term Signals From Wearable Sensors
In this work, we explore the discriminating ability of short-term signal patterns (e.g. few minutes long) with respect to the person identification task. We focus on signals recorded by simple wearable devices, such as smartwatches, which can measure move
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Multimodal Learning For Classroom Activity Detection
Classroom activity detection (CAD) focuses on accurately classifying whether the teacher or student is speaking and recording both the length of individual utterances during a class. A CAD solution helps teachers get instant feedback on their pedagogical
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Attention Guided Region Division For Crowd Counting
Crowd counting has drawn more and more attention in computer vision. There are two mainstream approaches to deal with crowd counting tasks, regression and detection. Regression-based methods usually overestimate the count in sparse areas, while detection-
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Speaker Independence Of Neural Vocoders And Their Effect On Parametric Resynthesis Speech Enhancement
Traditional speech enhancement systems produce speech with compromised quality. Here we propose to use the high quality speech generation capability of neural vocoders for better quality speech enhancement. We term this parametric resynthesis (PR). In pre
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Unsupervised Content-Preserved Adaptation Network For Classification Of Pulmonary Textures From Different Ct Scanners
Deep network based methods have been proposed for accurate classification of pulmonary textures on CT images. However, such methods well-trained on CT data from one scanner cannot perform well when they are directly applied to the data from other scanners
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Improving The Scalability Of Deep Reinforcement Learning-Based Routing With Control On Partial Nodes
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Machine Learning (ML)-based routing optimization has been proposed to optimize the performance of flow routing for future networks, such as Software-Defined Networks (SDNs). However, existing studies are either hard to converge for large networks or vulne
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Q-Gadmm: Quantized Group Admm For Communication Efficient Decentralized Machine Learning
In this paper, we propose a communication-efficient decentralized machine learning (ML) algorithm, coined quantized group ADMM (Q-GADMM). Every worker in Q-GADMM communicates only with two neighbors, and updates its model via the group alternating direct
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Speaker Diarization Using Latent Space Clustering In Generative Adversarial Network
In this work, we propose deep latent space clustering for speaker diarization using generative adversarial network (GAN) back-projection with the help of an encoder network. The proposed diarization system is trained jointly with GAN loss, latent variable
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Adaptive Distributed Stochastic Gradient Descent For Minimizing Delay In The Presence Of Stragglers
We consider the setting where a master wants to run a distributed stochastic gradient descent (SGD) algorithm on $n$ workers each having a subset of the data. Distributed SGD may suffer from the effect of stragglers, i.e., slow or unresponsive workers who
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Gpu-Accelerated Viterbi Exact Lattice Decoder For Batched Online And Offline Speech Recognition
We present an optimized weighted finite-state transducer (WFST) decoder capable of online streaming and offline batch processing of audio using Graphics Processing Units (GPUs). The decoder is efficient in memory utilization, input/output (I/O) bandwidth,
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Tree Of Shapes Cut For Material Segmentation Guided By A Design
In manufacturing, the monitoring of the fabrication process is crucial in order to be sure that objects are compliant. For nano-objects, most of this monitoring is done manually. In this paper, we propose a method to segment different materials in a manuf
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Variational Student: Learning Compact And Sparser Networks In Knowledge Distillation Framework
The holy grail in deep neural network research is porting the memory- and computation-intensive network models on embedded platforms with a minimal compromise in model accuracy. To this end, we propose Variational Student where we reap the benefits of com
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Rde-Moga: Automatic Selection Of Rate-Distortion-Energy Control Points For Video Encoders Using Muti-Objetive Genetic Algorithm
Controlling energy consumption of video encoders is acomplex multi-objective optimization problem of great im-portance. In this work we propose the RDE-MOGA, an multi-objective genetic algorithm capable of finding energeticallyefficient configurations for
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Emotional Voice Conversion Using Multitask Learning With Text-To-Speech
Voice conversion (VC) is a task that alters the voice of a person to suit different styles while conserving the linguistic content. Previous state-of-the-art technology used in VC was based on the sequence-to-sequence (seq2seq) model, which could lose lin
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A Novel Approach For Intelligibility Assessment In Dysarthric Subjects
Dysarthria is a motor speech impairment caused by muscle weakness. Individuals, with this condition, are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication.
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Sensor Selection For Model-Free Source Localization: Where Less Is More
The ability for a wireless network to precisely localize the radio nodes composing it is a great tool towards system optimization and is increasingly seen as a basic service requirement. In the past, model-free algorithms such as weighted centroid localiz