burst detection in time frequency space
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Burst detection in time-frequency space. Unfolding signal structure. Starting point unbiased toward specific signals. Aim at timing and frequency band accuracy. Nb: all results based on Virgo-type simulated data (LIGO-VIRGO joint group). AC Clapson. GWDAW 9, Annecy, December 2004.

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burst detection in time frequency space
Burst detection in time-frequency space
  • Unfolding signal structure.
  • Starting point unbiased toward specific signals.
  • Aim at timing and frequency band accuracy.

Nb: all results based on Virgo-type simulated data (LIGO-VIRGO joint group).

AC Clapson

GWDAW 9, Annecy, December 2004

s transform
S transform
  • Gaussian shaped complex exponential template.

(applied in frequency domain)

  • Resolution defined by Gaussian width and centre
    • Linear centre frequency spacing.
    • Width increases with frequency.
  • Colored spectrum issue
    • Spectrum reproduced in TF map.
    • Noise / signal discrimination based on excess energy: adaptive criterion.
  • Spectral lines issue
    • Line coupling with successive templates gives wide frequency profile.
    • Locally, line energy much larger than signal.
initial analysis chain
Initial analysis chain
  • Preprocessing
    • Line removal (Kalman filters).
    • High pass filter.
    • Segment edges Hann-shaped.
  • Transform
    • 215 bins long, exclude edges from map.
    • Output power.
    • Band 30 – 500 Hz.
    • Normalize mean and standard deviation

(for each frequency).

  • Event extraction
    • Bin energy cut.
    • Clusterize bins.
    • Cluster energy cut.
results
Results
  • Satisfying efficiency for sine Gaussians and short Gaussian peaks.
    • Energy well localized in TF map (compacity and frequency band)
  • Lower detectability for long Gaussian peak and DFM.
    • Energy reduction by preprocessing.
    • Spread out energy: secondary clusters.
    • Map frequency cut : high frequency component missed.
first improvement

Signals

First improvement

Map size increase:

  • Statistics more accurate.
  • Better frequency separation by template.
improvement under study

(Preliminary results)

Improvement under study
  • Non-linear list of template frequency centres.
    • Gaussian templates overlap set by equal s rule.
    • Reduces template number (1/3 here).
    • Requires energy renormalization (restore time-frequency atom integral)
summary
Summary
  • Core transform promising.
  • Compact signals efficiently extracted.
  • Complex signals require finer treatment.
  • Noise spectral features rapidly detrimental.
  • Series of modifications under study.
    • Alternative S transform
      • Implement “à la Welch” windowing (discard high pass).
      • Use non-linear frequency spacing (constant s spacing).
      • Avoid self-normalization.
    • Elaborate clustering / thresholding
      • Time coincidence before cluster energy cut.
      • Additional cluster parameter cuts (noise discrimination).

AC Clapson

kalman filters for line removal

Measure

Prediction

Correction

Model

Kalman filters for line removal
  • Relies on model of the feature to be extracted.
  • Discrete time first order recursive model => fast.
  • Optimal filter in Wiener sense (minimal reconstruction error)
  • Model mismatch detection

and correction required.

  • Model:
  • Thermally excited violin modes
    • Viscous damping oscillator equation
    • Parameters : m, Q, T, w0 , x0, x’0
template spacing
Template spacing
  • Balance centre frequency distance / template overlap.
    • fn+1 = fn + df
    • fn+1 = fn * sf
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