Estimation of noise from an image continues to be a challenging area of research in the field of image processing. However, noise estimation from images that inherently contains very fine details or which have textured regions is still a challenging task. This paper attempts to estimate noise content from images corrupted by Gaussian and Speckle noise. The noise estimation technique proposed he...

Noise estimation is an important part for noisy speech enhancement due to its momentous effect on the intelligibility and quality of the enhanced speech. In this paper, an effective noise estimation algorithm is presented by combining the minimum statistics estimation and Gaussian model assumption. In contrast to other methods, the proposed approach works in two steps. The noise power estimated...

We present sequential parameter estimation in the framework of the Hidden Markov Models. The sequential algorithm is a sequential Kullback proximal algorithm, which chooses the KullbackLiebler divergence as a penalty function for the maximum likelihood estimation. The scheme is implemented as £lters. In contrast to algorithms based on the sequential EM algorithm, the algorithm has faster conver...

The first step of missing feature methods in text-independent speaker identification is to identify highly corrupted spectrographic representation of speech as missing feature. Most mask estimation techniques rely on explicit estimation of the characteristics of the corrupting noise and usually fail to work with inaccurate estimation of noise. We present a mask estimation technique that uses ne...

One of the key factors enabling machine learning models to comprehend and solve real-world tasks is leverage multimodal data. Unfortunately, annotation data challenging expensive. Recently, self-supervised methods that combine vision language were proposed learn representations without annotation. However, these often choose ignore presence high levels noise thus yield sub-optimal results. In t...

Although most noise reduction algorithms are critically dependent on the noise power spectral density (PSD), most procedures for noise PSD estimation fail to obtain good estimates in nonstationary noise conditions. Recently, a DFT-subspace-based method was proposed which improves noise PSD estimation under these conditions. However, this approach is based on eigenvalue decompositions per DFT bi...

The implementation of three noise estimation algorithms using two different signal decomposition methods: a second-generation wavelet transform and a perceptual wavelet packet transform are described in this paper. The algorithms, which do not require the use of a speech activity detector or signal statistics learning histograms, are: a smoothing-based adaptive technique, a minimum variance tra...

Propagation of radio occultation (RO) signals through the lower troposphere results in high phase acceleration and low signal to noise ratio signal. The excess Doppler estimation accuracy in lower troposphere is very important in receiving RO signals which can be estimated by sliding window spectral analysis. To do this, various frequency estimation methods such as MUSIC and ESPRIT can be adopt...