In this paper, we propose a technique for removing a specific type of interference from a monaural recording. Nonstationary interferences are generally challenging to eliminate from such recordings. However, if the in...
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In this paper, we propose a technique for removing a specific type of interference from a monaural recording. Nonstationary interferences are generally challenging to eliminate from such recordings. However, if the interference is a known sound like a cell phone ringtone, music from a CD or streaming service, or a radio or TV broadcast, its source signal can be easily obtained. In our method, we define such interference as an acoustic object. Even if the sampling frequencies of the recording and the acoustic object do not match, we compensate for the mismatch and use the maximum likelihood estimation technique with the auxiliary function to remove the interference from the recording. We compare several probabilistic models for representing the object-canceled signal. Experimental evaluations confirm the effectiveness of our proposed method.
Sparse direction -of -arrival (DOA) estimation methods can be formulated as a group -sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent yea...
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Sparse direction -of -arrival (DOA) estimation methods can be formulated as a group -sparse optimization problem. Meanwhile, sparse recovery methods based on nonconvex penalty terms have been a hot topic in recent years due to their several appealing properties. Herein, this paper studies a new nonconvex regularized approach called the trimmed lasso for DOA estimation. We define the penalty term of the trimmed lasso in the multiple measurement vector model by l(2,1)-norm. First, we use the smooth approximation function to change the nonconvex objective function to the convex weighted problem. Next, we derive sparse recovery guarantees based on the extended Restricted Isometry Property and regularization parameter for the trimmed lasso in the multiple measurement vector problem. Our proposed method can control the desired level of sparsity of estimators exactly and give a more precise solution to the DOA estimation problem. Numerical simulations show that our proposed method overperforms traditional approaches, which is more close to the Cramer -Rao bound.
This study addresses the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation with bistatic multiple-input multiple-output (MIMO) radar. To the best of our knowledge, a limited numbe...
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This study addresses the problem of joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation with bistatic multiple-input multiple-output (MIMO) radar. To the best of our knowledge, a limited number of sparse Bayesian learning (SBL)-based methods exist that can be applied to joint DOD and DOA estimation. This is because of the heavy computational load and strong correlation between the nearby basis. To overcome these challenges, we present a new coarse non-uniformly sampled 2D grid and propose an improved SBL-based method for joint estimation of the DOD and DOA in MIMO radar. With the new grid, the computational load can be significantly reduced, and the nearby 2D grid points can provide a low correlation basis. To handle the modeling error derived from the coarse grid, we also introduce a modified linear approximation method into the SBL framework in which the locations of grid points are considered as adjustable parameters, and the grid points can be updated recursively. Finally, a block majorization-minimization algorithm is applied to perform Bayesian inference. Experimental results indicate that our method can improve the joint DOD and DOA estimation performance, particularly in the case of low signal-noise-ratio, limited snapshots, or correlated signals.
Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to ...
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Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weights and biases, and cost the expensive computations. Recently, some people have proposed a new frame for designing the deep networks according to the algorithms deduced from the l(2)-optimization problem. But they did not consider the case with outliers. Since l(1)-norm can describe the sparsity of the outliers better than l(2)-norm, we propose an effective deep network designed according to the new algorithm deduced from the l(1)-optimization problem. In our proposed method, an effective iterative algorithm for the l(1) reconstructed optimization problem is deduced based on the split Bregman algorithm, majorization-minimization algorithm, and soft thresholding operator. Then according to the deduced iterative algorithm, an effective deep network, named l(1) Model-Driven Recursive Multi-Scale Denoising Network (l(1)-MRMDN), is designed. Due to the iteration form of the deduced algorithm, the proposed l(1)-MRMDN contains an inner recursion and an outer recursion. Therefore, our proposed method can not only relieve its sensitiveness to the outliers because of the l(1) data fidelity term, but also avoid designing the deep network blindly via the guidance of prior knowledge. Extensive experimental results illustrate that our proposed method is superior to some related popular SISR methods. (C) 2021 Elsevier B.V. All rights reserved.
We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on gamma-divergence. A novel f...
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We develop a new robust geographically weighted regression method in the presence of outliers. We embed the standard geographically weighted regression in robust objective function based on gamma-divergence. A novel feature of the proposed approach is that two tuning parameters that control robustness and spatial smoothness are automatically tuned in a data-dependent manner. Further, the proposed method can produce robust standard error estimates and give us a reasonable quantity for local outlier detection. We demonstrate that the proposed method is superior to the existing robust geographically weighted regression through simulation and data analysis. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
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