Parametric Wiener Filter Research Articles

Drone audition: Audio signal enhancement from drone embedded microphones using multichannel Wiener filtering and Gaussian-mixture based post-filtering

In this paper, we consider the problem of recovering desired sound source signals from on-board microphone recordings on a noisy drone. Enhancement of source signal degraded by drone noise is considered to be a difficult task due to the strong noise generated from its motors and propellers causing an extremely low signal-to-drone noise ratio (SD‾NR). We propose a solution (i) by combining the widely known multichannel Wiener filter (MWF) to remove drone noise from microphone recordings, and (ii) further reduction of residual noise using a Gaussian mixture model (GMM) based dual-stage parametric Wiener filter (WF). The method exploits known statistics of motor current-specific drone noise. This combination of techniques to the specific context of signal enhancement for drone audition is applicable to irregular microphone arrays embedded on a drone enabling realistic integration to most drones. We demonstrate the validity of the proposed framework with extensive real data through (i) experimental recordings from two different drone acoustics datasets and (ii) outdoor measurements from a hovering drone for a bioacoustic application. The results confirm improved performance in terms of SD‾NR, speech quality (PESQ), and intelligibility (STOI) at very low SD‾NR (up to −30 dB) and show a strong potential for signal enhancement applications using noisy drones.

Accurate evaluation of self-heterodyne laser linewidth measurements using Wiener filters.

Self-heterodyne beat note measurements are widely used for the experimental characterization of the frequency noise power spectral density (FN-PSD) and the spectral linewidth of lasers. The measured data, however, must be corrected for the transfer function of the experimental setup in a post-processing routine. The standard approach disregards the detector noise and thereby induces reconstruction artifacts in the reconstructed FN-PSD. We introduce an improved post-processing routine based on a parametric Wiener filter that is free from reconstruction artifacts, provided a good estimate of the signal-to-noise ratio is supplied. Building on this potentially exact reconstruction, we develop a new method for intrinsic laser linewidth estimation that is aimed at deliberate suppression of unphysical reconstruction artifacts. Our method yields excellent results even in the presence of strong detector noise, where the intrinsic linewidth plateau is not even visible using the standard method. The approach is demonstrated for simulated time series from a stochastic laser model including 1/f-type noise.

Pre-study for facilitating the discovery of microfluidic properties in blood vessels using retinal fundus images

ABSTRACT COVID-19 disease may cause alterations of microfluidic properties of blood circulation in the retinal tissue. For the pre-study of microfluidic blood physiology and transport, retinal fundus images are applied for clinical screening of abnormal vessels forms. However, fundus images captured by operators with various levels of experience have diversity in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. Due to the optical beam of fundus image acquisition and vessel structure of the retina, natural image restoration methods cannot be applied straight to address this problem. The semi-automatic blind deblurring is a useful technique to restore the underlying sharp image given some assumed or known information about the cause of degradation. In this work, we propose a new hybrid algorithm for image restoration that does not require a priori knowledge of the noise distribution. The degraded image is first de-convoluted in Fourier space by parametric Wiener filtering; then, it is smoothed by using the anisotropic diffusion. The filtering model was tested on 177 fundus images. Experiment filtering results show the efficiency of our algorithm with a superlative performance (p-value < 0.05) when compared with state of the art methods.

Enhancement of Speech Intelligibility using Binary Mask Based on channel selection criteria

Most of the existing noise reduction algorithms used in hearing aid applications apply a gain function in order to reduce the noise intervention. In the present paper, we study the effect of the two types of speech distortions introduced by the gain functions. If these distortions are properly controlled large gains in intelligibility can be obtained. The sentences were corrupted by various kinds of noises i.e. babble noise, car noise, helicopter noise and random noise and processed through a noise-reduction algorithm. Subjective tests were conducted with normal hearing listeners by presenting the processed speech with controlled distortions. The method proposed by Kim et al uses the wiener filter. Here in this paper, we have used the parametric wiener filter. The experimental results clearly indicated improvement in intelligibility at 0dB, -5dB, +5dB and 10dB input signal-to-noise (SNR) values in short-time objective intelligibility (STOI) and Segmental signal-to-noise ratio (SSNR) objective measures.

Enhancement of Speech Intelligibility using Binary Mask Based on Noise Constraints

The primary aim of this paper is to examine the application of binary mask to improve intelligibility in most unfavorable conditions where hearing impaired/normal listeners find it difficult to understand what is being told. Most of the existing noise reduction algorithms are known to improve the speech quality but they hardly improve speech intelligibility. The paper proposed by Gibak Kim and Philipos C. Loizou uses the Weiner gain function for improving speech intelligibility. Here, in this paper we have proposed to apply the same approach in magnitude spectrum using the parametric wiener filter in order to study its effects on overall speech intelligibility. Subjective and objective tests were conducted to evaluate the performance of the enhanced speech for various types of noises. The results clearly indicate that there is an improvement in average segmental signal-to-noise ratio for the speech corrupted at -5dB, 0dB, 5dB and 10dB SNR values for random noise, babble noise, car noise and helicopter noise. This technique can be used in real time applications, such as mobile, hearing aids and speech–activated machines.

New insights on the optimality of parameterized Wiener filters for speech enhancement applications

This work presents a unified framework for defining a family of noise reduction techniques for speech enhancement applications. The proposed approach provides a unique theoretical foundation for some widely-applied soft and hard time-frequency masks, which encompasses the well-known Wiener filter and the heuristically-designed Binary mask. These techniques can now be considered as optimal solutions of the same minimization problem. The proposed cost function is defined by two design parameters that not only establish a desired trade-off between noise reduction and speech distortion, but also provide an insightful relationship with the mask morphology. Such characteristic may be useful for applications that require online adaptation of the suppression function according to variations of the acoustic scenario. Simulation examples indicate that the derived conformable suppression mask has approximately the same quality and intelligibility performance capability of the classical heuristically-defined parametric Wiener filter. The proposed approach may be of special interest for real-time embedded speech enhancement applications such as hearing aids and cochlear implants.

Operating sequence when noise is being filtered on distorted images

This paper discusses questions associated with the added noise of blurred and/or defocused images. The sequence of filtering the noise on such images—before eliminating the blurring/defocusing, or after it—is analyzed. The concepts of preliminary and subsequent filtering of noise are introduced. The blurring/defocusing of a series of images was eliminated by the methods of parametric Wiener filtering and Tikhonov regularization, while the noise was filtered out by the methods of adaptive Wiener filtering and median filtering.

The truncation – blurring – rotation technique for reconstructing distorted images

This paper discusses the problem of reconstructing distorted (smeared, defocused, noisy) grey and colored images. The smearing and defocusing of the images is eliminated by solving integral equations by the method of Tikhonov regularization or parametric Wiener filtering, while the noise is eliminated by the method of adaptive Wiener filtering or median filtering. A generalized technique of image truncation is proposed to replace the so-called boundary conditions, and a generalized technique of blurring the edges of the image is proposed to reduce the Gibbs effect. An image-rotation technique is proposed to model the smearing of an image at an arbitrary angle. The methods are implemented in the form of &lt;tt&gt;m&lt;/tt&gt;-files in the &lt;span style="font-variant: small-caps"&gt;MatLab&lt;/span&gt; system. Model and actual images are processed.

Three‐dimensional cortical dipole imaging of brain electrical activity considering spherical and median planes

AbstractEquivalent cortical dipole layer imaging is proposed for high‐resolution electroencephalogram (EEG) visualization. Equivalent dipole layer distribution parallel to the brain surface has been used in previous head models. In the present study, the additional dipole distribution on the median plane is introduced to identify the three‐dimensional (3D) position and orientation of dipole sources. The parametric Wiener filter (PWF) using statistical signal and noise information was applied to solve an inverse problem of cortical dipole imaging. Simulations were performed in an inhomogeneous, concentric, three‐sphere volume conductor head model under various signal conditions. Cortical dipole imaging provided localized brain electrical activity with better spatial resolution compared to the scalp potential map. These simulation results suggested that the depth and orientation of individual dipole sources could be observed by mapping on both spherical and median planes. We applied the proposed imaging technique to human experimental data of the visual evoked potential (VEP) and obtained reasonable results that coincide with physiological knowledge. © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging

We investigated suitable spatial inverse filters for cortical dipole imaging from the scalp electroencephalogram (EEG). The effects of incorporating statistical information of signal and noise into inverse procedures were examined by computer simulations and experimental studies. The parametric projection filter (PPF) and parametric Wiener filter (PWF) were applied to an inhomogeneous three-sphere volume conductor head model. The noise covariance matrix was estimated by applying independent component analysis (ICA) to scalp potentials. The present simulation results suggest that the PPF and the PWF provided excellent performance when the noise covariance was estimated from the differential noise between EEG and the separated signal using ICA and the signal covariance was estimated from the separated signal. Moreover, the spatial resolution of the cortical dipole imaging was improved while the influence of noise was suppressed by including the differential noise at the instant of the imaging and by adjusting the duration of noise sample according to the signal to noise ratio. We applied the proposed imaging technique to human experimental data of visual evoked potential and obtained reasonable results that coincide to physiological knowledge.

Performance comparison of a linear parametric noise estimation Wiener filter and non-linear joint transform correlator for realistic clutter backgrounds

It has been shown previously that a linear Wiener filter is capable of detecting a target in severe clutter backgrounds by utilising a parametric model of the clutter power spectrum in its filter transfer function. In this paper the performance of the linear Wiener filter is compared to that implemented in a non-linear joint transform correlator in which the entire current input scene is used as an approximation for the clutter background. Realistic clutter backgrounds are employed in the tests that cover a range of natural scenery likely to be encountered in practice. The linear Wiener filter, employing a parametric model of the averaged background scenes, is shown to outperform the non-linear filter in most cases. Brief consideration is also given to the relative merits of implementation of these two filters in both optical and digital correlators.

A priori and adaptive Wiener filtering with joint transform correlators

We propose general, simple, and powerful means of extending the capabilities of conventional joint-transform-correlator systems to enable a priori or adaptive Wiener filtering as well as parametric Wiener filtering of patterns corrupted by colored noise. Adaptive estimation of the noise power spectral density distribution may be obtained from input power spectra information and provides great flexibility and real-time adaptivity to colored noise, resulting in improved correlation performance.

Mutual relations among optimum image restoration filters

AbstractAn optimum image restoration filter is an image restoration filter satisfying some optimum criterion, and there are many different optimum restoration filters corresponding to different optimization criterion. Close relationships exist between these various filters, and through understanding these relationships we can, along with gaining a better understanding of the principles behind the filters, approximate or replace one filter by another and reduce the computing complexity required to compute it.The relationships between these filters have been explored on a one‐by‐one basis in the past, but in this paper a systematic assessment is made of the relationships between nine kinds of optimum restoration filters. In addition, it is confirmed experimentally that the parametric Wiener filter can approximate the averaged projection filter with arbitrary precision.

Railgun armature plasma-current density from deconvolved B-dot probe signals

It is shown that the spatial distribution of the moving armature current profile in an electromagnetic railgun can be recovered from the recorded signal of a B-dot probe. This approach differs from previous methods in that no a priori assumptions for the functional form of the armature current density are required. From certain simplifying assumptions, a model of the railgun circuit is developed from which the B-dot probe signal can be derived as a convolution of the armature current density with an impulse-response function dependent on the physical parameters of the experimental configuration. Deconvolution of the resulting signal corrupted with measurement noise is performed using a parametric Wiener filter. Simulated and actual numerical examples show that the physical length and overall shape of the armature current profile can be accurately determined using this method. However, the spatial resolution in the deconvolved signal is limited to approximately 1 to 2 cm for practical railgun applications. >