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A Low-Dimensionality Method For Data-Driven Graph Learning
In many graph signal processing applications, finding the topology of a graph is part of the overall data processing problem rather than a priori knowledge. Most of the approaches to graph topology learning are based on the assumption of graph Laplacian s
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Detection Of S1 And S2 Locations In Phonocardiogram Signals Using Zero Frequency Filter
Heart auscultation is a widely used technique for diagnosing cardiac abnormalities. In that context, capturing of phonocardiogram (PCG) signals and automatically monitoring of the heart by identifying S1 and S2 complexes is an emerging field. One of the f
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Multi-Time-Scale Convolution For Emotion Recognition From Speech Audio Signals
Robustness against temporal variations is important for emotion recognition from speech audio, since emotion is expressed through complex spectral patterns that can exhibit significant local dilation and compression on the time axis depending on speaker a
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Particle Group Metropolis Methods For Tracking The Leaf Area Index
Monte Carlo (MC) algorithms are widely used for Bayesian inference in statistics, signal processing, and machine learning. In this work, we introduce an Markov Chain Monte Carlo (MCMC) technique driven by a particle filter. The resulting scheme is a gener
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Stochastic Multi-Scale Aggregation Network For Crowd Counting
Crowd counting from unconstrained and congested scenes is an important task in computer vision. Its main difficulties stem from large scale/density variation and prone to overfitting. This paper presents a novel end-to-end stochastic multi-scale aggregati
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Spatial And Temporal Smoothing For Covariance Estimation In Super-Resolution Angle Estimation In Automotive Radars
Introduction of Digital Coded Modulation (DCM) radars in automotive applications has allowed large scale MIMO systems to be feasible within the operating cost and space constraints. Even with such large number of transceivers and intelligent array design,
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Gradient-Based Algorithm With Spatial Regularization For Optimal Sensor Placement
In this paper, we are interested in optimal sensor placement for signal extraction. Recently, a new criterion based on output signal to noise ratio has been proposed for sensor placement. However, to solve the optimization problem, a greedy approach is us
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X-Vectors Meet Emotions: A Study On Dependencies Between Emotion And Speaker Recognition
In this work, we explore the dependencies between speaker recognition and emotion recognition. We first show that knowledge learned for speaker recognition can be reused for emotion recognition through transfer learning. Then, we show the effect of emotio
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Deep Clustering With Concrete K-Means
We address the problem of simultaneously learning a k-means clustering and deep feature representation from unlabelled data, which is of interest due to the potential for deep k-means to outperform traditional two-step feature extraction and shallow clust
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Integration Of Multi-Look Beamformers For Multi-Channel Keyword Spotting
Keyword spotting (KWS) is in great demand in smart devices in the era of Internet of Things. Albeit recent progresses, the performance of KWS, measured in false alarms and false rejects, may still degrade significantly under the far field and noisy condit
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Individual Distance-Dependent Hrtfs Modeling Through A Few Anthropometric Measurements
The lack of data is a major problem in individual HRTF modeling. There are many HRTF databases, but each database only has limited HRTFs and has its own characteristics, such as distance-dependent HRTFs or individual HRTFs. How to effectively model HRTFs
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Adaptive Prediction Of Financial Time-Series For Decision-Making Using A Tensorial Aggregation Approach
Economic and financial decision-making may cause a significant impact on government, society, and industries. Due to the increasing volume of data, decision science has become an interdisciplinary field of study, supported by efficient methods and models
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Compressive 2-D Off-Grid Doa Estimation For Propeller Cavitation Localization
This paper introduces compressive sensing (CS) based two-dimensional (2-D) off-grid direction-of-arrival (DOA) estimation approach which can output the azimuths and elevations of radiating sources for propeller tip vortex cavitation localization. With a d
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Intra Frame Rate Control For Versatile Video Coding With Quadratic Rate-Distortion Modelling
With numerous coding tools adopted in the forthcoming Versatile Video Coding (VVC) standard, much less work has been dedicated to study the corresponding Rate-Distortion (R-D) characteristics. This paper proposes a new quadratic R-D model for Versatile Vi
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Unified Signal Compression Using Generative Adversarial Networks
We propose a unified compression framework that uses generative adversarial networks (GAN) to compress image and speech signals. The compressed signal is represented by a latent vector fed into a generator network which is trained to produce high quality
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Learned Lossless Image Compression With A Hyperprior And Discretized Gaussian Mixture Likelihoods
Lossless image compression is an important task in the field of multimedia communication. Traditional image codecs typically support lossless mode, such as WebP, JPEG2000, FLIF. Recently, deep learning based approaches have started to show the potential a
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Self-Training For End-To-End Speech Recognition
We revisit self-training in the context of end-to-end speech recognition. We demonstrate that training with pseudo-labels can substantially improve the accuracy of a baseline model. Key to our approach are a strong baseline acoustic and language model use
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Residual Recurrent Neural Network For Speech Enhancement
Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn the temporal correlations acros
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Tracking To Improve Detection Quality In Lidar For Autonomous Driving
Enabling Lidar systems to detect objects at very long ranges has the potential to be extremely valuable for autonomous driving applications, but is challenging due to noise. In this work, we leverage information from multiple consecutive frames to improve
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Sketchppnet: A Joint Pixel And Point Convolutional Neural Network For Low Resolution Sketch Image Recognition
Sketch recognition using deep neural networks have become a recent trend. However, traditional pixel (image) based convolutional neural networks show poor recognizing performance on low resolution (LR) sketch image due to the loss of image details. To sol
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Leveraging Cuboids For Better Motion Modeling In High Efficiency Video Coding
In conventional video compression systems, motion model is used to approximate the geometry of moving object boundaries. It is possible to relieve motion model from describing discontinuities in the underlying motion field, by incorporating motion hint th
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Exploiting Commutativity Condition For Cp Decomposition Via Approximate Simultaneous Diagonalization
In this paper, we propose a novel strategy which utilizes an inherent algebraic property of simultaneously diagonalizable matrix tuples, i.e., commutativity, for both (i) reducing approximate CP decomposition of a higher-order tensor to Approximate Simult
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Dynamic Temporal Residual Learning For Speech Recognition
Long short-term memory (LSTM) networks have been widely used in automatic speech recognition (ASR). This paper proposes a novel dynamic temporal residual learning mechanism for LSTM networks to better explore temporal dependencies in sequential data. The
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Deep Joint Source-Channel Coding For Wireless Image Retrieval
Motivated by surveillance applications with wireless cameras or drones, we consider the problem of image retrieval over a wireless channel. Conventional systems apply lossy compression on query images to reduce the data that must be transmitted over a ban
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Mask-Dependent Phase Estimation For Monaural Speaker Separation
Speaker separation refers to isolating speech of interest in a multi-talker environment. Most methods apply real-valued Time-Frequency (T-F) masks to the mixture Short-Time Fourier Transform (STFT) to reconstruct the clean speech. Hence there is an unavoi
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Robust Global Optimized Affine Registration Method For Microscopic Images Of Biological Tissue
Affine registration can fit the non-rigid deformation of slices effectively, and it is widely used in volume reconstruction of biological tissue. But most of the existing affine registration methods are registered in a given sequence, which results in the
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Motion Dynamics Improve Speaker-Independent Lipreading
We present a novel lipreading system that improves on the task of speaker-independent word recognition by decoupling motion and content dynamics. We achieve this by implementing a deep learning architecture that uses two distinct pipelines to process moti
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Modeling Piece-Wise Stationary Time Series
We consider the problem of modeling piece-wise stationary time series. We propose a new, data-driven technique to automatically identify change-points and learn piece-wise stationary models. We do not assume prior knowledge of the stationary models or the
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Information Maximized Variational Domain Adversarial Learning For Speaker Verification
Domain mismatch is a common problem in speaker verification. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) to reduce domain mismatch by incorporating an InfoVAE into domain adversarial training (DAT
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Object Detection And 3D Estimation Via An Fmcw Radar Using A Fully Convolutional Network
This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orien
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Conditional Domain Adversarial Transfer For Robust Cross-Site Adhd Classification Using Functional Mri
There is a growing number of large scale cross-site database collection of resting-state functional magnetic resonance imaging (rs-fMRI) for studying neurobehavioral diseases, such as ADHD. Although a large amount of data benefits machine learning-based c
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Full-Reference Speech Quality Estimation With Attentional Siamese Neural Networks
In this paper, we present a full-reference speech quality prediction model with a deep learning approach. The model determines a feature representation of the reference and the degraded signal through a siamese recurrent convolutional network that shares
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Addressing The Confounds Of Accompaniments In Singer Identification
Identifying singers is an important task with many applications. However, the task remains challenging due to many issues. One major issue is related to the confounding factors from the background instrumental music that is mixed with the vocals in music
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Combining Deep Embeddings Of Acoustic And Articulatory Features For Speaker Identification
In this study, deep embedding of acoustic and articulatory features are combined for speaker identification. First, a convolutional neural network (CNN)-based universal background model (UBM) is constructed to generate acoustic feature (AC) embedding. In
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Deep Exposure Fusion With Deghosting Via Homography Estimation And Attention Learning
Modern cameras have limited dynamic ranges and often produce images with saturated or dark regions using a single exposure. Although the problem could be addressed by taking multiple images with different exposures, exposure fusion methods need to deal wi
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Robust Cfar Radar Detection Using A K-Nearest Neighbors Rule
The problem of robust radar detection is addressed from a machine learning inspired perspective. In particular, a novel interpretation of the well-known Kelly's and adaptive matched filter (AMF) detectors is provided in terms of decision region boundaries
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L1-Norm Higher-Order Orthogonal Iterations For Robust Tensor Analysis
Standard Tucker tensor decomposition seeks to maximize the L2-norm of the compressed tensor; thus, it is very responsive to outlying/high-magnitude entries among the processed data. To counteract the impact of outliers in tensor data analysis, we propose
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Underwater Tracking Based On The Sum-Product Algorithm Enhanced By A Neural Network Detections Classifier
The necessity of long-range underwater surveillance has strongly increased in the last decades, and low-frequency active sonar (LFAS) systems seem to fulfill this need. However, in littoral environments with shallow water LFAS may suffer from an elevate n
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Using Panoramic Videos For Multi-Person Localization And Tracking In A 3D Panoramic Coordinate
3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In
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Hybrid Autoregressive Transducer (Hat)
This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoder-decoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the qu
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Automatic Identification Of Speakers From Head Gestures In A Narration
In this work, we focus on quantifying speaker identity information encoded in the head gestures of speakers, while they narrate a story. We hypothesize that the head gestures over a long duration have speaker-specific patterns. To establish this, we consi
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Salient Object Detection Based On Image Bit-Map
In this paper, we propose a novel salient object detection framework, which makes full use of the essential image compression. More specifically, we first compose an intuitive measure of compressibility from JPEG compression, namely bit-map. Then, dependi
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The Empirical Duality Gap Of Constrained Statistical Learning
This paper is concerned with the study of constrained statistical learning problems, the unconstrained version of which are at the core of virtually all of modern information processing. Accounting for constraints, however, is paramount to incorporate pri
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Dense Mapping Of Intracellular Diffusion And Drift From Single-Particle Tracking Data Analysis
It is of primary interest for biologists to be able to visualize diffusion and drift dynamics of proteins within the cell. In this paper, we propose a new mapping method to robustly estimate dynamics in the entire cell from particle tracks. To obtain sati
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Investigation Of Specaugment For Deep Speaker Embedding Learning
SpecAugment is a newly proposed data augmentation method for speech recognition. By randomly masking bands in the log Mel spectogram this method leads to impressive performance improvements. In this paper, we investigate the usage of SpecAugment for speak
