This paper presents a statistical algorithm using Maximum A Posteri­ ori (MAP) estimation for the enhancement of single channel speech， contami­ nated by the additive noise， under the blind framework. The algorithm uses Generalized Gaussian Distribution (GGD) function as a prior probability to model magnitude of the Spectral Components (SC) of the speech and noise in the frequency domain. An es...

One of the key issues in removing random-valued impulse noise from digital images using switching filters is the impulse noise detection. Impulse noise is a random, spiked variation in the brightness of the image. In this paper, a new impulse noise detection algorithm is presented that is based on Noise ratio Estimation and a combination of K-means clustering and Non-Local Means based filter (N...

Many optical flow estimation techniques are based on the differential optical flow equation. These algorithms involve solving over-determined systems of optical flow equations. Least squares (LS) estimation is usually used to solve these systems even though the underlying noise does not conform to the model implied by LS estimation. To ameliorate this problem, work has been done using the total...

It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is proposed for identification of noisy input-output ...

It is shown that under certain conditions it is possible to obtain a good speech estimate from noise without requiring noise estimation. We study an implementation of the theory, namely wide matching, for speech enhancement. The new approach performs sentence-wide joint speech segment estimation subject to maximum recognizability to gain noise robustness. Experiments have been conducted to eval...

In this paper, a speech enhancement algorithm is proposed to improve the speech intelligibility for cochlear implant recipients. Our method is based on combination of harmonic estimation and traditional statistical method. Traditional statistical based speech enhancement method is effective only for stationary noise suppression, but not non-stationary noise. To address more complex noise scenar...

Partition noise is closely related to reset noise and has been observed in detection nodes of reset transistor architecture in image sensors. This work presents the analysis of partition noise based on an improved technique for estimation of charge distribution in the transistor channel at any given time instant. We incorporate the transistor turn off transients by taking into account both drif...

In this paper, we attempt to generalize the ideal binary mask (IBM) estimation to the ideal ratio mask (IRM) estimation. Under binary masking, the error in IBM estimation may greatly distort the original speech spectrum. The main purpose of this paper is using ratio mask to smooth this negative impact. Since the key issue is the noise tracking, we firstly use exponential distributions to model ...

In this paper, we show that a hierarchical Bayesian modelling approach to sequential learning leads to many interesting attributes such as regularisation and automatic relevance determination. We identify three inference levels within this hierarchy, namely model selection, parameter estimation and noise estimation. In environments where data arrives sequentially, techniques such as cross-valid...

We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and noise estimation. In environments where data arrive sequentially, techniques such as cross validation to achieve regularization or model selection are not possible. The Bayesian app...