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.

Hybrid feedforward-feedback active noise reduction for hearing protection and communication.

Abstract: A hybrid active noise reduction (ANR) architecture is presented and validated for a circumaural earcup and a communication earplug. The hybrid system combines source-independent feedback ANR with a Lyapunov-tuned leaky LMS filter (LyLMS) improving gain stability margins over feedforward ANR alone. In flat plate testing, the earcup demonstrates an overall C-weighted total noise reduction of 40dB and 30–32dB, respectively, for 50–800Hz sum-of-tones noise and for aircraft or helicopter cockpit noise, improving low frequency (<100Hz) performance by up to 15dB over either control component acting individually. For the earplug, a filtered-X implementation of the LyLMS accommodates its nonconstant cancellation path gain. A fast time-domain identification method provides a high-fidelity, computationally efficient, infinite impulse response cancellation path model, which is used for both the filtered-X implementation and communication feedthrough. Insertion loss measurements made with a manikin show overall C-weig...

ECG Denoising Using Marginalized Particle Extended Kalman Filter With an Automatic Particle Weighting Strategy

Abstract: In this paper, a model-based Bayesian filtering framework called the “marginalized particle-extended Kalman filter (MP-EKF) algorithm” is proposed for electrocardiogram (ECG) denoising. This algorithm does not have the extended Kalman filter (EKF) shortcoming in handling non-Gaussian nonstationary situations because of its nonlinear framework. In addition, it has less computational complexity compared with particle filter. This filter improves ECG denoising performance by implementing marginalized particle filter framework while reducing its computational complexity using EKF framework. An automatic particle weighting strategy is also proposed here that controls the reliance of our framework to the acquired measurements. We evaluated the proposed filter on several normal ECGs selected from MIT-BIH normal sinus rhythm database. To do so, artificial white Gaussian and colored noises as well as nonstationary real muscle artifact (MA) noise over a range of low SNRs from 10 to −5 dB were added to these normal ECG segments. The benchmark methods were the EKF and extended Kalman smoother (EKS) algorithms which are the first model-based Bayesian algorithms introduced in the field of ECG denoising. From SNR viewpoint, the experiments showed that in the presence of Gaussian white noise, the proposed framework outperforms the EKF and EKS algorithms in lower input SNRs where the measurements and state model are not reliable. Owing to its nonlinear framework and particle weighting strategy, the proposed algorithm attained better results at all input SNRs in non-Gaussian nonstationary situations (such as presence of pink noise, brown noise, and real MA). In addition, the impact of the proposed filtering method on the distortion of diagnostic features of the ECG was investigated and compared with EKF/EKS methods using an ECG diagnostic distortion measure called the “Multi-Scale Entropy Based Weighted Distortion Measure” or MSEWPRD. The results revealed that our proposed algorithm had the lowest MSEPWRD for all noise types at low input SNRs. Therefore, the morphology and diagnostic information of ECG signals were much better conserved compared with EKF/EKS frameworks, especially in non-Gaussian nonstationary situations.

System for coring an image-representing signal

Abstract: Noise reduction is achieved, without the introduction of noticeable artifacts in the displayed image, using (1) a non-ringing, non-aliasing, localized transfer, octave-band spectrum analyzer for separating the video signal representing the image into subspectra signals, (2) separate coring means for one or more of the analyzed subspectra signals, and (3) then a synthesizer employing one or more non-ringing, non-aliasing filters for deriving an output image-representing signal from all of the subspectra signals.

Noise Reduction of Atmospheric Emitted Radiance Interferometer (AERI) Observations Using Principal Component Analysis

Abstract: A principal component noise filter has been applied to ground-based high-spectral-resolution infrared radiance observations collected by the Atmospheric Emitted Radiance Interferometers (AERIs) deployed by the Atmospheric Radiation Measurement (ARM) program. The technique decomposes the radiance observations into their principal components, selects the ones that describe the most variance in the data, and reconstructs the data from these components. An empirical function developed for chemical analysis is utilized to determine the number of principal components to be used in the reconstruction of the data. Statistical analysis of the noise-filtered minus original radiance data, as well as side-by-side analysis of data from two AERI systems utilizing different temporal sampling, demonstrates the ability of the noise filter using this empirical function to retain most of the atmospheric signal above the AERI noise level in the filtered data. The noise filter is applied to data collected at ARM’s tr...