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The Wiener filter is a well-known signal processing method for improving a noisy signal's quality. The Wiener filter requires either knowledge of or estimates of the power spectra of the signal-of-interest and of the undesired noise, leading to implementation challenges. In this paper, we show how a recently-developed second-order signal quantity termed the panorama can be employed to compute the...
We propose an algorithm to uncover the intrinsic low-rank component of a high-dimensional, graph-smooth and grossly-corrupted dataset, under the situations that the underlying graph is unknown. Based on a model with a low-rank component plus a sparse perturbation, and an initial graph estimation, our proposed algorithm simultaneously learns the low-rank component and refines the graph. The refined...
Traditional speech separation systems enhance the magnitude response of noisy speech. Recent studies, however, have shown that perceptual speech quality is significantly improved when magnitude and phase are both enhanced. These studies, however, have not determined if phase enhancement is beneficial in environments that contain reverberation as well as noise. In this paper, we present an approach...
Speech recognition performance deteriorates in face of unknown noise. Speech enhancement offers a solution by reducing the noise in speech at runtime. However, it also introduces artificial distortions to the speech signals. In this paper, we aim at reducing the artifacts that has adverse effects on speech recognition. With this motivation, we propose a modification scheme including smoothing adaptation...
This paper is concerned with optimal estimation of the state of a Boolean dynamical systems observed through correlated noisy Boolean measurements. The optimal Minimum Mean-Square Error (MMSE) state estimator for general Partially-Observed Boolean Dynamical Systems (POBDS) can be computed via the Boolean Kalman Filter (BKF). However, thus far in the literature only the case of white observation noise...
This paper considers the problem of estimating an unknown high dimensional signal from (typically low-dimensional) noisy linear measurements, where the desired unknown signal is assumed to possess a group-sparse structure, i.e. given a (pre-defined) partition of its entries into groups, only a small number of such groups are non-zero. Assuming the unknown group-sparse signal is generated according...
MMSE filtering of signals contaminated with additive noise is addressed with explicit uncertainty of the second-order target signal statistics. The unfortunate lack of stationarity of speech, and hence the phenomenon of musical noise in speech enhancement, is an ideal problem for the proposed approach. Specifically, we complement the established short-time power-spectral subtraction for speech power...
This paper considers asymptotic perfect secrecy and asymptotic perfect estimation in distributed estimation for large sensor networks under threat of an eavesdropper, which has access to all sensor outputs. To measure secrecy, we compare the estimation performance at the fusion center and at eavesdropper in terms of their respective Fisher Information. We analyze the Fisher Information ratio between...
The paper proposes a convex combination fusion function based on a sigmoid function for the estimation of the a priori SNR in a speech enhancement framework with critical frequency band processing. The proposed method does not only eliminate the one frame delay generated by the well-known decision directed approach but also increases the adaptation speed during abrupt changes in the SNR estimation...
Recently it has been shown that specific classes of non-bandlimited signals known as signals with finite rate of innovation (FRI) can be perfectly reconstructed by using appropriate sampling kernels and reconstruction schemes. The knowledge of the model order (i.e. the rate of innovation) is essential for correct reconstruction. In view of this, we devise an algorithm which can robustly identify the...
Recently, time-frequency mask-based beamforming has been extensively studied as the frontend of deep neural network (DNN) based automatic speech recognition (ASR) in noisy environments. Two mask estimation approaches have been separately developed for this beamforming method, namely the the DNN-based approach, which exploits the time-frequency features of the signal, and the spatial clustering-based...
Supervised speech separation algorithms seldom utilize output patterns. This study proposes a novel recurrent deep stacking approach for time-frequency masking based speech separation, where the output context is explicitly employed to improve the accuracy of mask estimation. The key idea is to incorporate the estimated masks of several previous frames as additional inputs to better estimate the mask...
In this paper, we present an optimal multi-channel Wiener filter, which consists of an eigenvector beamformer and a single-channel postfilter. We show that both components solely depend on a speech presence probability, which we learn using a deep neural network, consisting of a deep autoencoder and a softmax regression layer. To prevent the DNN from learning specific speaker and noise types, we do...
A novel patch-based multi-view image denoising algorithm is proposed. This method leverages the 3D focus image stacks structure to exploit self-similarity and image redundancy inherent in multiple view images. Then a depth-guided adaptive window and dynamic view selection criterion is developed to aid proper selection of most consistent patches for the multi-view image denoising. Extensive experiments...
Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated...
Location detection or localization supporting navigation has assumed significant importance in the recent past. In particular, techniques that exploit cheap inertial measurement units (IMU), the gyroscope and the accelerometer, have garnered attention, especially in an embedded computing context. However, these sensors measurements are quite unreliable, and it is widely believed that these sensors...
The advancements in automatic speech recognition through pre-processing of speech is an alternative way to improve word recognition rate. In this work, we explored the speech enhancement techniques for improving the performance of automatic speech recognition for degraded dysarthric speech. The spectral floor parameter is optimized in the multi-band spectral subtraction technique as it controls the...
Estimation of bias field together with the tissue class of a noisy Magnetic Resonance image has been a challenging task because of the nonlinear nature of bias field. In order to address this issue we have proposed two new schemes. The first one is the recursive framework, where class labels and bias fields have been estimated simultaneously. In one part of the recursion, a variable variance Adaptive...
Noise estimation plays an essential role in enhancing the performance of non-coherent spectrum sensors such as energy detectors. If the noise energy is misestimated, detector performance may deteriorate. In this paper, we present an energy detector based on the behavior that Empirical Mode Decomposition (EMD) has on noise-only channels. EMD decomposes time-series signals into a finite set of components...
The regularization parameter is required in most (if not all) adaptive algorithms, while its role becomes very critical in the presence of additive noise. In this paper, we focus on the regularized recursive least-squares (RLS) algorithm and present a method to find its regularization parameter, which is related to the signal-to-noise ratio (SNR). Also, using a proper estimation of the SNR, we further...
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