scholarly journals Differences Amplitude Based Improvement of Minimum Variance Distortionless Response Filter

2020 ◽  
Vol 8 (5) ◽  
pp. 1635-1637
Keyword(s):  
A Priori ◽  
Minimum Variance ◽  
Coherent Noise ◽  
Noise Interference ◽  
Power Spectral ◽  
Minimum Variance Distortionless Response ◽  
Power Spectral Densities ◽  
Noisy Signals ◽  
A New Technique ◽  
Priori Information

In this work, the author introduces a new technique for improving the performance of minimum variance distortionless response filter in condition of coherent noise. The proposal algorithm exploits a priori information of differences amplitude to balance power spectral densities of observed noisy signals. The output signal of MVDR filter is then processed by an additional post-filtering, which based speech presence probability to suppress more noise interference and increase quality speech. In experiments using two noisy signal recordings in anechoeic room, the modified MVDR-filter results provides that the suggested algorithm increases speech quality compared to the conventional MVDR filter.

scholarly journals A Speech Enhancement MVDR Algorithm in Diffuse Noise Field

2020 ◽  
Vol 9 (3) ◽  
pp. 2309-2311
Keyword(s):  
Speech Enhancement ◽  
Minimum Variance ◽  
Noise Field ◽  
Power Spectral ◽  
Minimum Variance Distortionless Response ◽  
Power Spectral Densities ◽  
Real Complex ◽  
Diffuse Noise ◽  
Target Speaker

The minimum variance distortionless response (MVDR) beamformer often used in speech application for separating sound source and suppressing ambient, coherent, stationary and non-stationary noise in real complex environment. This paper deals problem speech enhancement in diffuse noise field by using a modified MVDR, which incorporates speech presence probability to estimate auto and cross power spectral densities. This combination gives the advantage of saving target speaker while suppressing background noise. A efficiency post-filtering, which is a function depends on speech presence probability, used for increasing the quality of filtered signal. The performance evaluation demonstrates the ability of proposal algorithm when compared to conventional MVDR.

scholarly journals A new approach to nonparametric spectral density estimation

2021 ◽  
Author(s):  
Sudeshna Pal
Keyword(s):  
Spectral Density ◽  
Density Estimation ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
New Approach ◽  
Spectral Density Estimation ◽  
Noise Interference ◽  
Novel Approach ◽  
Power Spectral ◽  
Noisy Signals

A novel approach to nonparametric spectral density estimation has been proposed. The approach is based on a new evaluation criterion called autocorrelation mean square error (AMSE) for power spectral density (PSD) estimates of available finite length data. Minimization of this criterion not only provides the optimum segmentation for existing PSDE approaches , but also provides a new optimum windowing within the segments that can be combined additionally to the existing methods of nonparametric PSDE. Furthermore, the problem of frequency resolution in existing PSDE methods for noisy signals has been analyzed. In the existing approaches, the additive noise and the finiteness of data which are the causes of the original loss of the frequency resolution are not treated separately. The suggested new approach to spectrum estimation takes advantage of these two different causes of the problem and tackles the problem of resolution in two steps. First, the method optimally reduces noise interference with the signal via minimum noiseless description length (MNDL). The new power spectrum estimation MNDL-Periodogram of the denoised signal is then computed via conventional indirect periodogram to improve frequency resolution.

scholarly journals A new approach to nonparametric spectral density estimation

2021 ◽  
Author(s):  
Sudeshna Pal
Keyword(s):  
Spectral Density ◽  
Density Estimation ◽  
Frequency Resolution ◽  
Spectrum Estimation ◽  
New Approach ◽  
Spectral Density Estimation ◽  
Noise Interference ◽  
Novel Approach ◽  
Power Spectral ◽  
Noisy Signals

A novel approach to nonparametric spectral density estimation has been proposed. The approach is based on a new evaluation criterion called autocorrelation mean square error (AMSE) for power spectral density (PSD) estimates of available finite length data. Minimization of this criterion not only provides the optimum segmentation for existing PSDE approaches , but also provides a new optimum windowing within the segments that can be combined additionally to the existing methods of nonparametric PSDE. Furthermore, the problem of frequency resolution in existing PSDE methods for noisy signals has been analyzed. In the existing approaches, the additive noise and the finiteness of data which are the causes of the original loss of the frequency resolution are not treated separately. The suggested new approach to spectrum estimation takes advantage of these two different causes of the problem and tackles the problem of resolution in two steps. First, the method optimally reduces noise interference with the signal via minimum noiseless description length (MNDL). The new power spectrum estimation MNDL-Periodogram of the denoised signal is then computed via conventional indirect periodogram to improve frequency resolution.

Pitfalls of using conventional and discrete Radon transforms on poorly sampled data

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1467-1482 ◽  
Author(s):  
Kurt J. Marfurt ◽  
Robert V. Schneider ◽  
Michael C. Mueller
Keyword(s):  
A Priori ◽  
Least Square ◽  
Radon Transforms ◽  
Sampled Data ◽  
A Priori Information ◽  
Amplitude Analysis ◽  
Coherent Noise ◽  
Analysis Window ◽  
P Domain ◽  
Priori Information

The least‐square discrete Radon transform (DRT) is currently one of the most popular methods used in the suppression of multiples and other coherent noise events on irregularly sampled data gathers used for prestack true amplitude analysis. Unfortunately, in the absence of a priori information, this technique suffers from the same aliasing problems as Fourier and conventional (τ, p) methods. Although the DRT is able to reconstruct the original image more accurately than conventional (τ, p) transforms, a harmful by‐product is an increase in the amplitude of aliased events in the transform domain. In particular, the DRT will boost the amplitude of the aliases of true events that fall outside the p analysis window to help reconstruct the input data. These amplified aliases degrade signal periodicity in the (τ, p) domain. If muted, they can destroy subtle amplitude changes necessary for amplitude variation with offset (AVO) analysis. At the very least, one should carefully evaluate the choice of analysis window and mutes when designing a filter in the (τ, p) domain. Alternatively, one can exploit additional a priori information based on semblance. Iterative application of the DRT and mutes can also be used to suppress aliased events further.

scholarly journals COV-OBS.x2: 180 years of geomagnetic field evolution from ground-based and satellite observations

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Loïc Huder ◽  
Nicolas Gillet ◽  
Christopher C. Finlay ◽  
Magnus D. Hammer ◽  
Hervé Tchoungui
Keyword(s):  
Geomagnetic Field ◽  
Field Model ◽  
Formal Model ◽  
A Priori ◽  
Magnetic Data ◽  
Core Field ◽  
The Core ◽  
Power Spectral ◽  
Ill Posed ◽  
Priori Information

Abstract We present the geomagnetic field model COV-OBS.x2 that covers the period 1840–2020. It is primarily constrained by observatory series, satellite data, plus older surveys. Over the past two decades, we consider annual differences of 4-monthly means at ground-based stations (since 1996), and virtual observatory series derived from magnetic data of the satellite missions CHAMP (over 2001–2010) and Swarm (since 2013). A priori information is needed to complement the constraints carried by geomagnetic records and solve the ill-posed geomagnetic inverse problem. We use for this purpose temporal cross-covariances associated with auto-regressive stochastic processes of order 2, whose parameters are chosen so as to mimic the temporal power spectral density observed in paleomagnetic and observatory series. We aim this way to obtain as far as possible realistic posterior model uncertainties. These can be used to infer for instance the core dynamics through data assimilation algorithms, or an envelope for short-term magnetic field forecasts. We show that because of the projection onto splines, one needs to inflate the formal model error variances at the most recent epochs, in order to account for unmodeled high frequency core field changes. As a by-product of the core field model, we co-estimate the external magnetospheric dipole evolution on periods longer than 2 years. It is efficiently summarized as the sum of a damped oscillator (of period 10.5 years and decay rate 55 years), plus a short-memory (6 years) damped random walk.

Naturally smooth inversions with a priori information from well logs

Geophysics ◽  
1991 ◽  
Vol 56 (11) ◽  
pp. 1811-1818 ◽  
Author(s):  
M. Pilkington ◽  
J. P. Todoeschuck
Keyword(s):  
Inverse Problem ◽  
A Priori ◽  
Power Spectral Analysis ◽  
Well Logs ◽  
Noise Model ◽  
Model Parameters ◽  
Scaling Exponent ◽  
A Priori Information ◽  
Power Spectral ◽  
Priori Information

Regularization is usually necessary to guarantee a solution to a given inverse problem. When constructing a model that gives an adequate fit to the data, some suitable method of regularization which provides numerical stability can be used. When investigating the resolution and variance of the computed model parameters, the character of regularization should be specified by the a priori information available. This avoids arbitrary variation of the damping to suit the interpreter. For geophysical inverse problems we determine the appropriate level of regularization (in the form of parameter covariances) from power spectral analysis of well‐log measurements. For resistivity data, well logs indicate that the spatial variation with depth can be described adequately by a scaling noise model, that is, one in which the power spectral density is proportional to some power (α) of the frequency. We show that α, the scaling exponent, controls the smoothness of the final model. For α < 0, the model becomes smoother as α becomes more negative. As a specific example, this approach is applied to the magnetotelluric inverse problem. A synthetic example illustrates the smoothing effect of α on inversion. Comparison between the scaling noise approach and a previous Backus‐Gilbert type inversion on some field data shows that using the appropriate value of α (−1.8 for this example) results in a model which is structurally simple and contains only those features well resolved by the data.

scholarly journals An integrated MVDR beamformer for speech enhancement using a local microphone array and external microphones

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Randall Ali ◽  
Toon van Waterschoot ◽  
Marc Moonen
Keyword(s):  
Speech Enhancement ◽  
Speech Processing ◽  
Speech Intelligibility ◽  
Microphone Array ◽  
A Priori ◽  
Minimum Variance ◽  
Quadratic Program ◽  
Acoustic Environment ◽  
Speech Distortion ◽  
Minimum Variance Distortionless Response

AbstractAn integrated version of the minimum variance distortionless response (MVDR) beamformer for speech enhancement using a microphone array has been recently developed, which merges the benefits of imposing constraints defined from both a relative transfer function (RTF) vector based on a priori knowledge and an RTF vector based on a data-dependent estimate. In this paper, the integrated MVDR beamformer is extended for use with a microphone configuration where a microphone array, local to a speech processing device, has access to the signals from multiple external microphones (XMs) randomly located in the acoustic environment. The integrated MVDR beamformer is reformulated as a quadratically constrained quadratic program (QCQP) with two constraints, one of which is related to the maximum tolerable speech distortion for the imposition of the a priori RTF vector and the other related to the maximum tolerable speech distortion for the imposition of the data-dependent RTF vector. An analysis of how these maximum tolerable speech distortions affect the behaviour of the QCQP is presented, followed by the discussion of a general tuning framework. The integrated MVDR beamformer is then evaluated with audio recordings from behind-the-ear hearing aid microphones and three XMs for a single desired speech source in a noisy environment. In comparison to relying solely on an a priori RTF vector or a data-dependent RTF vector, the results demonstrate that the integrated MVDR beamformer can be tuned to yield different enhanced speech signals, which may be more suitable for improving speech intelligibility despite changes in the desired speech source position and imperfectly estimated spatial correlation matrices.

Innovative Approach to Information Search by Example of a Patent Analysis of an Important Substitution Plan

2020 ◽  
pp. 143-157
Author(s):  
Maria A. Milkova
Keyword(s):  
Information Search ◽  
Topic Modeling ◽  
Cognitive Biases ◽  
A Priori ◽  
Import Substitution ◽  
Innovative Approach ◽  
Iterative Search ◽  
Comprehensive Picture ◽  
Priori Information ◽  
Selection Of

Nowadays the process of information accumulation is so rapid that the concept of the usual iterative search requires revision. Being in the world of oversaturated information in order to comprehensively cover and analyze the problem under study, it is necessary to make high demands on the search methods. An innovative approach to search should flexibly take into account the large amount of already accumulated knowledge and a priori requirements for results. The results, in turn, should immediately provide a roadmap of the direction being studied with the possibility of as much detail as possible. The approach to search based on topic modeling, the so-called topic search, allows you to take into account all these requirements and thereby streamline the nature of working with information, increase the efficiency of knowledge production, avoid cognitive biases in the perception of information, which is important both on micro and macro level. In order to demonstrate an example of applying topic search, the article considers the task of analyzing an import substitution program based on patent data. The program includes plans for 22 industries and contains more than 1,500 products and technologies for the proposed import substitution. The use of patent search based on topic modeling allows to search immediately by the blocks of a priori information – terms of industrial plans for import substitution and at the output get a selection of relevant documents for each of the industries. This approach allows not only to provide a comprehensive picture of the effectiveness of the program as a whole, but also to visually obtain more detailed information about which groups of products and technologies have been patented.

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