Noise reduction

About: Noise reduction is a research topic. Over the lifetime, 25121 publications have been published within this topic receiving 300815 citations. The topic is also known as: denoising & noise removal.

Inverse and Reciprocity Methods for Machinery Noise Source Characterization and Sound Path Quantification Part 1: Sources

Abstract: A conventional approach for the analysis of noise control problems uses the source-transmission path-receiver scheme In many situations this type of analysis is complicated by the fact that there is a multitude of simultaneous sources Moreover, even for a single noise source like a machine, there can be a multitude of partial sources and "parallel" transmission paths Therefore, the development of cost-effective noise reduction strategies often requires detailed knowledge of the contributions of the partial sources and transmission paths to the radiated sound at distant receiver positions Generally speaking, this type of analysis requires a system modelling in terms of inputs I, which genuinely characterize the sources themselves and of output-input ratios O/I These latter are transfer functions TF, which characterize the transmission paths Such an analytical approach facilitates the effective specification of required noise source quietening and of improved sound or vibration isolation in one or more paths The treatments in this article will be limited to systems with supposedly linear behaviour, ie to systems for which the output response may be modelled as a linear superposition of all contributions of the partial sources and transmission paths Then in loose mathematical terms the following model equations apply:

Phase Noise Estimation and Mitigation for OFDM Systems

Abstract: OFDM suffers from severe performance degradation in the presence of phase noise. In particular, phase noise leads to common phase error (CPE) as well as intercarrier interference (ICI) in the frequency domain. Some approaches in the literature mitigate phase noise by directly evaluating and then compensating for CPE or ICI, while others choose to correct phase noise in the time domain. A new parametric model of OFDM signals is proposed in this paper which shows that, in the presence of phase noise, each received frequency-domain subcarrier signal can be expressed as a sum of all subcarrier signals weighted by a vector parameter. Then, two reduced-complexity techniques are presented to estimate this weighting vector. The first is a maximum likelihood (ML) method whereas the second one is a linear minimum mean square error (LMMSE) technique. Using the obtained estimates, we also propose two approaches, i.e., a decorrelator and an interference canceler, to mitigate phase noise. It is shown that most conventional methods can be readily obtained from our approaches with some approximation or orthogonal transform. Theoretical analysis and numerical results are provided to elaborate the proposed schemes. We show that the performance of both approaches is superior to that of conventional methods. Furthermore, LMMSE gives the best performance, while ML provides a much simpler yet effective way to mitigate phase noise

Medical Image Noise Reduction Using the Sylvester–Lyapunov Equation

Abstract: Multiplicative noise is often present in medical and biological imaging, such as magnetic resonance imaging (MRI), ultrasound, positron emission tomography (PET), single photon emission computed tomography (SPECT), and fluorescence microscopy. Noise reduction in medical images is a difficult task in which linear filtering algorithms usually fail. Bayesian algorithms have been used with success but they are time consuming and computationally demanding. In addition, the increasing importance of the 3-D and 4-D medical image analysis in medical diagnosis procedures increases the amount of data that must be efficiently processed. This paper presents a Bayesian denoising algorithm which copes with additive white Gaussian and multiplicative noise described by Poisson and Rayleigh distributions. The algorithm is based on the maximum a posteriori (MAP) criterion, and edge preserving priors which avoid the distortion of relevant anatomical details. The main contribution of the paper is the unification of a set of Bayesian denoising algorithms for additive and multiplicative noise using a well-known mathematical framework, the Sylvester-Lyapunov equation, developed in the context of the control theory.

Method for enhancing image data by noise reduction or sharpening

Abstract: An image enhancing process promotes either noise reduction or image sharpening on a pixel-by-pixel basis as a function of the recognition of specific patterns of sampled pixel values surrounding each pixel to be enhanced The sampled pixel values are divided into two or more subgroups and the enhanced pixel value is provided to promote either image sharpening or noise reduction as a function of the number of pixel values in the different subgroups and their positions relative to each other in the subgroups

Noise reduction and audio-visual speech activity detection

Abstract: The present invention generally relates to the field of noise reduction systems which are equipped with an audio-visual user interface, in particular to an audio-visual speech activ­ity recognition system (200b/c) of a video-enabled telecommunication device which runs a real-time lip tracking application that can advantageously be used for a near-speaker detection algorithm in an environment where a speaker's voice is interfered by a statistically distributed background noise (n'(t)) including both environmental noise (n(t)) and surrounding persons' voices