One of the leading speech enhancement technologies is the deep neural network-based approach, which dominates the recent development in single-channel speech enhancement. In this paper, we propose a convolutional network with multi-scale and attention mechanisms for end-to-end single-channel speech enhancement (MASENet). More specifically, the MASENet network consists of five modules, namely multi-scale

An online censoring (OC) based weighted-frequency Fourier linear combiner (OC-WFLC) adaptive filtering structure is proposed to reduce data processing costs in the estimation of pathological hand tremor (PHT) measurements. The proposed OC-WFLC is combined with the Fourier linear combiner (FLC) to effectively separate the PHT and voluntary movement from the hand tremor signal. The OC-WFLC is shown to

Neither a monocular RGB camera nor a small-size microphone array is capable of accurate three-dimensional (3D) speaker localization. By taking advantage of accurate visual object detection, and audio-visual complementary sensor fusion, we formulate the three-dimensional (3D) speaker localization problem as a visual scaling factor estimation problem. As a result, we effectively reduce the traditional

We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices with enhanced generalization abilities. Extensive

In this letter, an acoustic imaging method is proposed to improve conventional non-synchronous measurements (NSM) by carrying out the NSM at coprime positions (CP-NSM). The pattern of the NSM movement is guided by a set of coprime positions. A virtual domain signal is then constructed by vectorizing the covariance matrix of the NSM. Similarly, a virtual array is constructed using the Kronecker product

The modality and pose variance between RGB and infrared (IR) images are two key challenges for RGB-IR person re-identification. Existing methods mainly focus on leveraging pixel or feature alignment to handle the intra-class variations and cross-modality discrepancy. However, these methods are hard to keep semantic identity consistency between global and local representation, which the consistency

In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency for the samples of the confusing classes. Class-based expansion learning takes a bottom-up growing strategy in a class-based expansion optimization fashion, which pays more attention to the quality of learning the fine-grained

Power-Based Capon beamforming has recently been proposed to avoid the well-known cancellation effects between direct signal and correlated multipaths of the Capon beamformer. This novel technique exploits the fact that in some applications the power of the direct signal can be known at the receiver, in addition to the more usual assumptions of known spatial and temporal signatures. At the implementation

Visual redundancy detection is essential for image and video communication. Human visual system (HVS) is difficult to perceive the pixel magnitude change below a certain visibility threshold which is also known as just-noticeable-difference (JND). In this letter, we present an efficient JND estimation approach for screen content images by considering high-frequency sensitivity and orientation sensitivity

Improving intelligibility of a speech signal of interest from its observations (with a single microphone) corrupted by additive noise has long been a challenging problem. Motivated by important findings achieved in the psychoacoustic field, we propose in this work a deep learning based method to render the noise and desired speech in the perceptual space such that the perception of the desired speech

In this letter, we proposed a novel unsupervised learning strategy for direction-of-arrival (DOA) estimation network. Inspired by the sparse power spectrum and $\ell _1$ -norm optimization, we develop a novel loss function to cooperate with the estimation network. Unlike the prior DL-based methods, the proposed method does not need any manual annotations for training and validation datasets. Compared

This letter studies the problem of Direction of Arrival (DoA) estimation from low-resolution few-bit quantized data collected by Sparse Linear Array (SLA). In such cases, contrary to the one-bit quantization case, the well known arcsine law cannot be employed to estimate the covaraince matrix of unquantized array data. Instead, we develop a novel optimization-based framework for retrieving the covaraince

In recent years, correntropy-based algorithms which include maximum correntropy criterion (MCC), generalized MCC (GMCC), kernel MCC (KMCC) and hyperbolic cosine function-based algorithms such as hyperbolic cosine adaptive filter (HCAF), logarithmic HCAF (LHCAF), least lncosh (Llncosh) have been widely utilized in adaptive filtering due to their robustness towards non-Gaussian/impulsive background noises

Person re-identification (ReID) aims to match pedestrian images across disjoint cameras. Existing supervised ReID methods utilize deep networks and train them with identity-labeled images, which suffer from limited annotations. Recently, clustering-based unsupervised ReID attracts more and more attention. It first clusters unlabeled images and assigns cluster index to the pseudo-identity-labels, then

Generalized zero-shot learning (GZSL) poses a challenging problem in that it aims to recognize both seen classes that have appeared in the training stage and unseen classes that have not appeared during training. By utilizing a gating mechanism as the binary classifier, gating methods can decompose GZSL into a conventional ZSL problem and a supervision learning task, thereby leading to outstanding

In this letter, a novel nested array is proposed which not only increases the degrees of freedom (DOF) but also reduces the mutual coupling that occurs in the general nested array structure by moving the original array only half wavelength. Here we deal with real-valued sources like AM or BPSK whereby we can explore the property of the sum coarray along with the difference coarray of the synthetic

This work proposes a method to acquire the geometry of a room in terms of the size and shape by exploiting the signal propagation times of the single bound reflections (SBRs). The information of room geometry is crucial for improving localization and assisting robot navigation under complex indoor environments. The data association problem for the SBR time measurements from the same reflection surface

We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On the technology front, we offer a simple and straightforward extension to the now widely used x-vector. It consists of an auxiliary neural net predicting the frame-wise uncertainty of the input sequence. We show that the proposed

Recently, the performances of deep convolution neural networks (CNNs)-based single-image super-resolution (SISR) have been significantly improved. However, most of the existing CNN-based SISR methods mainly focus on wider or deeper networks and ignore the potential relationship between multi-scale features, leading to the limited representation ability of the reconstructed network. To address this

Phase synchronization phenomena are directly connected with the underlying neural mechanisms of certain cognitive processes. However, only the amplitude information is utilized in most electroencephalogram (EEG)-based brain–computer interfaces (BCIs). Few of the existing methods can simultaneously measure the amplitude and phase information required for classification. In this study, a novel common

In this work, we consider the target detection problem in a sensing architecture where the radar is aided by a reconfigurable intelligent surface (RIS), that can be modeled as an array of sub-wavelength small reflective elements capable of imposing a tunable phase shift to the impinging waves and, ultimately, of providing the radar with an additional echo of the target. A theoretical analysis is carried

Sparse signal recoveries from multiple measurement vectors (MMV) with joint sparsity property have many applications in signal, image, and video processing. The problem becomes much more involved when snapshots of the signal matrix are temporally correlated. With signal's temporal correlation in mind, we provide a framework of iterative MMV algorithms based on thresholding, functional feedback and

Road detection is an important task inthe signal processing field. Although self-supervised learning has the potential to learn rich and effective visual representations that avoid tedious labeling, the current approaches learn from object-centered images, which leads to ambiguous results in complex traffic scenarios. We introduce saliency estimation to extend the self-supervised segmentation beyond

This letter addresses the constrained target tracking problem based on the approach of the trajectory function of time (T-FoT). Both state equality and inequality constraints such as the trajectory geometry and width are considered, respectively. The penalty function is used in the T-FoT framework to account for the constraint. Specifically, the logarithmic barrier function is used to solve the challenging

Pixel-value-ordering (PVO) is one of the most widely used reversible data hiding (RDH) framework which efficiently utilizes smooth pixels of the cover image to provide high-fidelity stego-image but with limited embedding capacity. This letter proposes an RDH scheme based on local moment driven pixel value ordering (LM-PVO) which further enhances the effectiveness of smooth pixel's utilization by dividing

Deep convolutional neural networks have achieved great success in many computer vision tasks. However, they can be attacked by adversarial examples which are input-data with small intentional feature perturbations to fool machine learning models. This vulnerability to adversarial examples poses a potential threat to their widespread application, especially in security-sensitive scenarios. In this paper

This paper aims to construct optimal Z-complementary code set (ZCCS) with non-power-of-two (NPT) lengths to enable interference-free multicarrier code-division multiple access (MC-CDMA) systems. The existing ZCCSs with NPT lengths, which are constructed from generalized Boolean functions (GBFs), are sub-optimal only with respect to the set size upper bound. For the first time in the literature, we

In this paper, a new filter in the nonlinear Kalman filtering framework is proposed. The new filter is referred to as the lattice Kalman filter (LKF) and is based on a class of quasi-Monte Carlo (QMC) methods known as lattice rules. The proposed LKF method uses the Korobov type lattice rule to deterministically generate sample points that are randomly shifted based on the Cranley-Patterson shift method

Images captured behind glass are often contaminated by the undesired reflection that hinders other computer vision tasks. Due to ubiquitous glasses, the removal of undesired reflection becomes more important. Reflection is determined by imaging system parameters such as the transparency, color of the glasses, the position of the camera. In this letter, we propose a novel generative model based on a

Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task is quite challenging and has not been fully studied to date. Current works mainly focus on body shape or contour sketch, but they are not robust enough due to view and posture variations. The key to this task is to exploit cloth-irrelevant cues. This

3D mask face presentation attack detection (PAD) plays a vital role in securing face recognition systems from emergent 3D mask attacks. Recently, remote photoplethysmography (rPPG) has been developed as an intrinsic liveness clue for 3D mask PAD without relying on the mask appearance. However, the rPPG features for 3D mask PAD are still needed expert knowledge to design manually, which limits its further

Recently, the performance of super-resolution has been improved by the stereo images since the additional information could be obtained from another view. However, it is a challenge to interact the cross-view information since disparities between left and right images are variable. To address this issue, we propose a disparity feature alignment module (DFAM) to exploit the disparity information for

In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based

How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for point cloud semantic segmentation, named as GA-Net, consisting of a point-independent global attention module and a point-dependent global attention module for obtaining contextual information of 3D point clouds in this paper

Recently, Retinex theory based low-light image enhancement (LLIE) algorithms have achieved impressive results in controlled environment. However, the majority of deep learning based LLIE algorithms leverage relighting by enhancing the illumination components that directly determines the image brightness, regretfully, they ignore the information of reflectance components, which may cause problems such

The Versatile Video Coding (VVC) achieves superior coding efficiency as compared with the High Efficiency Video Coding (HEVC), while its excellent coding performance is at the cost of several high computational complexity coding tools, such as Quad-Tree plus Multi-type Tree (QTMT)-based Coding Units (CUs) and multiple inter prediction modes. To reduce the computational complexity of VVC, a CNN-based

Low-rank tensor completion (LRTC) aims to impute the missing entries from partially observed tensor data, among which low-rankness is of vital importance to get satisfactory results. In this letter, we propose a hierarchical low-rank factorization framework for high-order tensors. For the first layer, the low TR rank is exploited, and for the second layer the low-rankness of each TR core is further

The constant Q transform (CQT) has been shown to be one of the most effective speech signal pre-transforms to facilitate synthetic speech detection, followed by either hand-crafted (subband) constant Q cepstral coefficient (CQCC) feature extraction and a back-end binary classifier, or a deep neural network (DNN) directly for further feature extraction and classification. Despite the rich literature

On the Euclidean domains of classical signal processing, linking of signal samples to underlying coordinate structures is straightforward. While graph adjacency matrices totally define the quantitative associations among the underlying graph vertices, a major problem in graph signal processing is the lack of explicit association of vertices with an underlying coordinate structure. To make this link

Channel pruning has become one of the predominant compression methods to deploy deep models on resource-constrained devices. Most channel pruning methods often use a fixed compression rate for all the layers of the model, which, however, may not be optimal. To address this issue, given a specific target compression rate, one can search for the optimal compression rate for each layer via some automated

JPEG images are usually corrupted by various undesirable compression artifacts resulted from block-wise coarse quantization on discrete cosine transform coefficients. In recent years, deep convolutional neural networks (CNNs) have made spectacular achievements in compression artifacts reduction. However, most deep CNNs are difficult to be implemented on mobile devices due to their large number of parameters

Cross Z-complementary pairs (CZCPs) are a special kind of Z-complementary pairs having zero autocorrelation sums around the in-phase position and end-shift position, also having zero cross-correlation sums around the end-shift position. Recent results have shown that CZCPs are very efficient in designing pilot sequences for spatial modulation enabled multiple-input multiple-output (MIMO) systems. In

The two-dimensional (2-D) Z-complementary array pair (ZCAP) can be viewed as an extension of the well-known one-dimensional (1-D) Z-complementary pair (ZCP). To date, most constructions of 2-D ZCAPs are indirect and based on existing 1-D sequences or 2-D arrays. In this letter, a new direct construction of 2-D ZCAPs with flexible array sizes based on 2-D generalized Boolean functions is proposed. Compared

Person re-identification in the wild is often challenged by occlusion. Existing methods mainly rely on learned external cues like pose or parsing to ease occlusion distraction. This knowledge highly related to body semantics may introduce alignment effects, leading to additional requirements for dedicated training data and inference computation. We propose the Self-guided Body Part Alignment method

This letter focuses on adaptive target detection in the presence of multiple interference sources, which comprise clutter, thermal noise, noise-like jammers, and fully-correlated (or coherent) signals. In order to account for different operating scenarios, we formulate the problem at hand in terms of a multiple hypothesis test with several alternative hypotheses representative of each considered scenario

One of the main advantages of second-order methods in a centralized setting is that they are insensitive to the condition number of the objective function's Hessian. For applications such as regression analysis, this means that less pre-processing of the data is required for the algorithm to work well, as the ill-conditioning caused by highly correlated variables will not be as problematic. Similar

In this paper we propose a long-term prediction method for low delay transform domain general audio coders. This Frequency Domain Joint Harmonics Prediction (FDJHP) method operates directly in the Modified Discrete Cosine Transform (MDCT) domain and can enhance the coding efficiency, even under very low frequency resolutions. We compare this new method with state-of-the-art MDCT based methods by analyzing

Within a single speech emotion corpus, deep neural networks have shown decent performance in speech emotion recognition. However, the performance of the emotion recognition based on data-driven learning methods degrades significantly for the cross-corpus scenario. To relieve this issue without any labeled samples from the target domain, we propose a cross-corpus speech emotion recognition based on

We develop a framework for generalized linear transformations of the joint spatial-Slepian domain representation of signals on the sphere. Such a representation is enabled by the spatial-Slepian transform on the sphere. We formulate a least-square signal estimation framework for reconstruction of the spherical signal from the modified (transformed) spatial-Slepian representation specified by the spatial-Slepian

Recent studies of driving scene understanding based on image semantic segmentation have achieved dramatic advances in speed and accuracy. Large-scale public datasets for semantic segmentation of paved road driving scenes have led the advances, but there is no large-scale public dataset for unpaved road environments. Building a large-scale image semantic segmentation dataset for unpaved roads is very

RGB-D image classification based on convolutional neural networks have been extensively explored recently. However, they suffer from problems of effective representation of RGB-D image, intra-class variances and inter-class similarities. To address these problems, this letter proposes a novel RGB-D image classification framework based on reduced biquaternion stacked denoising convolutional autoencoder

The Cramér-Rao bound (CRB) for direction of arrival (DOA) estimation exploiting both auto-correlation and cross-correlation information within multiple frequencies of the received array signals is derived. It provides a tighter bound than the existing CRB for the dual-frequency scenario. For the multiple frequencies, it is much lower than its dual-frequency counterpart, and also exists for a greater

Texture classification is one of the essential problems in computer vision. Due to the powerful feature extraction ability, convolutional neural network (CNN) based texture classification methods have attracted extensive attention in recent years. However, there are still some challenges, such as the extraction of multi-level texture features and their relationships. To address these problems, this

The next-item recommendation has been in the central of interest in real-world applications such as e-commerce. However, it is challenging to infer what a user may purchase next due the complex interactions in the historical sessions and the changing semantics of an item over time. Most existing methods employ separate models to generate the general preference and the sequential patterns for the next-item

This letter proposes a time-domain method to improve speech intelligibility in noisy scenarios. In the proposed approach, a series of Gammatone filters are adopted to detect the harmonic components of speech. The filters outputs are amplified to emphasize the first harmonics, reducing the masking effects of acoustic noises. The proposed GTF $_\text{F0}$ solution and two baseline techniques are examined

With the wide vision and high flexibility, unmanned aerial vehicle (UAV) has been widely used into object tracking in recent years. However, its limited computing capability poses a great challenges to tracking algorithms. On the other hand, Discriminative Correlation Filter (DCF) based trackers have attracted great attention due to their computational efficiency and superior accuracy. Many studies

In this paper we propose a powerful frequency, phase angle, and amplitude estimation solution for an unbalanced three-phase power system based on multiple model adaptive estimation. The proposed model utilizes the existence of a conditionally linear and Gaussian substructure in the power system states by marginalizing out the frequency component. This substructure can be effectively tracked by a bank

The rank of the sparse signals brought by multiple measurement vectors (MMV) augments the performance of joint sparse recovery. In general, suppose the sparsity level $ k$ is less than or equal to $ [rank(\boldsymbol{X})+spark(\boldsymbol{A})-1]/2$ , the sparsest solution of the MMV problem is unique and recoverable via various methods. It is shown in this letter that the unique solution of the sparsity

Convergence of the diffusion RLS (DRLS) algorithm to steady-state has been extensively studied in the literature, whereas no analysis of its transient convergence behavior has been reported yet. In this letter, we conduct a theoretical analysis of the transient behavior of the DRLS algorithm for cyclostationary colored inputs, in the mean and mean-square error sense. The resulting analytical models

This letter proposes a novel blind acoustic mask (BAM) designed to adaptively detect noise components and preserve target speech segments in time domain. A robust standard deviation estimator is applied to the non-stationary noisy speech to identify noise masking elements. The main contribution of the proposed solution is the use of this noise statistics to derive an adaptive information to define