Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A statistical Wiener filter using complex analysis of variance

J C Woestenburg, M N Verbaten, W P Sjouw

    Biological Psychology
    |December 1, 1981
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

    Articles linked to this work by shared authors, journal, and citation graph.

    Sort by
    Same author

    Concurrent Schedules: Effects of Blackout during the Changeover Delay.

    Behavioural processes·2014
    Same author

    Serotonergic drug effects on a delayed conditional discrimination task in the rat; involvement of the 5-HT1A receptor in working memory.

    Journal of psychopharmacology (Oxford, England)·2012
    Same author

    Deterioration of executive functioning in chronic ecstasy users; evidence for multiple drugs effects.

    Current drug abuse reviews·2010
    Same author

    Effects of object complexity and type on the gaze behavior of children with pervasive developmental disorder.

    Brain and cognition·2007
    Same author

    No effects of l-dopa and bromocriptine on psychophysiological parameters of human selective attention.

    Journal of psychopharmacology (Oxford, England)·2006
    Same author

    Stopping and changing in adults with ADHD.

    Psychological medicine·2005

    This study introduces a statistical Wiener filter to improve signal detection in noisy data. By analyzing variance, it adaptively sets filter coefficients, reducing distortion and enhancing signal extraction compared to traditional methods.

    Area of Science:

    • Signal processing
    • Statistical analysis
    • Digital filtering

    Background:

    • The Wiener filter detects signals in noise using limited data series (N).
    • High variance and negative coefficients in Wiener filters cause signal distortion, especially with low N or low signal-to-noise ratios.
    • Current solutions, like clipping negative coefficients, offer limited practical value.

    Purpose of the Study:

    • To develop a statistically robust Wiener filter method for improved signal extraction.
    • To address signal distortion caused by inaccurate noise-power estimation in low N scenarios.
    • To enhance noise suppression in signal-in-noise series.

    Main Methods:

    • Utilizing complex analysis of variance to differentiate signal and noise spectra.

    Related Experiment Videos

  • Implementing a statistical procedure to adjust noise-power estimates.
  • Setting transfer coefficients (H(w)'s) to zero if they do not meet a significance criterion.

    • Main Results:

    • The statistical Wiener filter functions as an adaptive multi-band-pass filter.
    • Band-passes are determined by detected signal components.
    • Demonstrated superior noise suppression in signal-in-noise series compared to classical methods.

    Conclusions:

    • The proposed statistical Wiener filter effectively mitigates distortion caused by inaccurate noise estimation.
    • This method provides a statistically sound approach to Wiener filtering, improving signal detection.
    • The adaptive nature of the filter allows for optimized noise suppression based on signal characteristics.