Power line channels are often badly corrupted by impulsive noise. In this paper, an impulsive noise cancellation method for MIMO power line communication is proposed. First, the received signal is transformed into frequency domain and MIMO detection is carried out. Then, the detected signal is transformed back to time domain and the noise estimation can be obtained by subtracting detected signa...

Recently, acoustic models based on deep neural notworks (DNNs) have been introduced and showed dramatic improvements over acoustic models based on GMM in a variety of tasks. In this paper, we considered the improvement of noise robustness of DNN. Inspired by Missing Feature Theory and static noise aware training, we proposed an approach that uses a noise-suppressed acoustic feature and estimate...

INTRODUCTION Data analysis in MRI is sophisticated and can be thought of as a “pipeline” of closely connected processing and modeling steps. Because noise in MRI data affects all subsequent steps in this pipeline, e.g., from noise reduction and image registration to parametric tensor estimation [1] and uncertainty assessment [2], accurate noise assessment has an important role in MRI studies. N...

This paper describes cross-correlation based systems for the estimation of the room impulse response. A various types of excitation signals are used: white noise, pink noise, swept-sine, MLS, and random phase multisine. Criteria to evaluate the quality of the impulse response estimation were: a) random and stationary noise rejection, b) rejection of nonlinearities introduced by measuring system...

In this paper, a new adaptive robustified filter algorithm of recursive weighted least squares with combined scale and variable forgetting factors for time-varying parameters estimation in non-stationary and impulsive noise environments has been proposed. To reduce the effect of impulsive noise, whether this situation is stationary or not, the proposed adaptive robustified approach extends the ...

Estimation of the power spectrum S( f ) of a stationary random process can be viewed as a nonparametric statistical estimation problem. We introduce a nonparametric approach based on a wavelet representation for the logarithm of the unknown S( f ). This approach offers the ability to capture statistically significant components of lnS( f ) at different resolution levels and guarantees nonnegati...

We describe a novel algorithm for recursive estimation of nonstationary acoustic noise which corrupts clean speech, and a successful application of the algorithm in the speech feature enhancement framework of noise-normalized SPLICE for robust speech recognition. The noise estimation algorithm makes use of a nonlinear model of the acoustic environment in the cepstral domain. Central to the algo...

In this paper, an adaptive noise estimation technique is proposed on the basis of non-negative matrix factorization (NMF). As an initial step of the proposed method, the noise basis matrix of NMF is estimated from a collection of noise signals. Then, the proposed method updates the initially estimated noise basis matrix on the fly by using an estimate of the noise spectrum from the noisy signal...

We present a simple yet effective algorithm for noise reduction of speech signals using a lattice LP filter. Based on previous investigations and a theoretical analysis of the lattice filter parameter estimation we introduce an improved parameter estimation algorithm that takes into account the non-stationary nature of speech and expected noise signals, yielding a good suppression of stationary...

We address precision of estimation of the degree of polarization (DOP) from the orthogonal state contrast image (OSCI) in the presence of both signal-dependent Poisson noise due to useful signal, and additive Poisson noise due to dark current and / or background light. We determine the Cramer Rao Lower Bound and deduce from it figures of merit for DOP estimation. In particular, we show that the...