Reassignment method: Difference between revisions

Content deleted Content added
m The method of reassignment: align instead of matrix
Line 111:
== The method of reassignment ==
 
Pioneering work on the method of reassignment was published by Kodera, Gendrin, and de Villedary under the name of ''Modified Moving Window Method'' <ref>{{cite journal |author1=K. Kodera |author2=R. Gendrin |author3=C. de Villedary |last-author-amp=yes |date=Feb 1978 |title=Analysis of time-varying signals with small BT values |journal=IEEE Transactions on Acoustics, Speech and Signal Processing |volume=26 |issue=1 |pages=64–76 | publisher= |doi=10.1109/TASSP.1978.1163047 |url= |accessdate= }}</ref> Their technique enhances the resolution in time and frequency of the classical Moving Window Method (equivalent to the spectrogram) by assigning to each data point a new time-frequency coordinate that better-reflects the distribution of energy in the analyzed signal.
Pioneering work on the method of reassignment was
published by Kodera, Gendrin, and de Villedary under the
name of ''Modified Moving Window Method''
<ref>
{{cite journal |author1=K. Kodera |author2=R. Gendrin |author3=C. de Villedary |last-author-amp=yes |date=Feb 1978 |title=Analysis of time-varying signals with small BT values |journal=IEEE Transactions on Acoustics, Speech and Signal Processing |volume=26 |issue=1 |pages=64–76 | publisher= |doi=10.1109/TASSP.1978.1163047 |url= |accessdate= }}
</ref>
Their technique enhances the resolution in time and
frequency of the classical Moving Window Method (equivalent
to the spectrogram) by assigning to each data point a new
time-frequency coordinate that better-reflects the
distribution of energy in the analyzed signal.
 
In the classical moving window method, a time-___domain signal, <math>x(t)</math> is decomposed into a set of coefficients, <math>\epsilon( t, \omega )</math>, based on a set of elementary signals, <math>h_{\omega}(t)</math>, defined
signal, <math>x(t)</math> is decomposed into a set of
coefficients, <math>\epsilon( t, \omega )</math>, based on a set of elementary signals, <math>h_{\omega}(t)</math>,
defined
 
:<math>h_{\omega}(t) = h(t) e^{j \omega t} </math>
<center><math>
h_{\omega}(t) = h(t) e^{j \omega t}
</math></center>
 
where <math>h(t)</math> is a (real-valued) lowpass kernel function, like the window function in the short-time Fourier transform. The coefficients in this decomposition are defined
function, like the window function in the short-time Fourier
transform. The coefficients in this decomposition are defined
 
<center>:<math>\begin{align}
\epsilon( t, \omega ) &= \int x(\tau) h( t - \tau ) e^{ -j \omega \left[ \tau - t \right]} d\tau \\
&= \int x(\tau) h( t - \tau ) e^{ -j \omega \left[ \tau - t \right]} d\tau \\
&= e^{ j \omega t} \int x(\tau) h( t - \tau ) e^{ -j \omega \tau } d\tau \\
&= e^{ j \omega t} X(t, \omega) \\
&= X_{t}(\omega) \\
&= M_{t}(\omega) e^{j \phi_{\tau}(\omega)}
\end{align}</math></center>
 
where <math>M_{t}(\omega)</math> is the magnitude, and <math>\phi_{\tau}(\omega)</math> the phase, of <math>X_{t}(\omega)</math>, the Fourier transform of the signal <math>x(t)</math> shifted in time by <math>t</math> and windowed by <math>h(t)</math>.
<math>\phi_{\tau}(\omega)</math> the phase, of
<math>X_{t}(\omega)</math>, the Fourier transform of the
signal <math>x(t)</math> shifted in time by <math>t</math>
and windowed by <math>h(t)</math>.
 
<math>x(t)</math> can be reconstructed from the moving window coefficients by
 
<center>:<math>\begin{align}
x(t) & = \iint X_{\tau}(\omega) h^{*}_{\omega}(\tau - t) d\omega d\tau \\
& = \iint X_{\tau}(\omega) h( \tau - t ) e^{ -j \omega \left[ \tau - t \right]} d\omega d\tau \\
&= \iint M_{\tau}(\omega) e^{j \phi_{\tau}(\omega)} h( \tau - t ) e^{ -j \omega \left[ \tau - t \right]} d\omega d\tau \\
&= \iint M_{\tau}(\omega) h( \tau - t ) e^{ j \left[ \phi_{\tau}(\omega) - \omega \tau+ \omega t \right] } d\omega d\tau
\end{align}</math></center>
 
For signals having magnitude spectra, <math>M(t,\omega)</math>, whose time variation is slow relative to the phase variation, the maximum contribution to the reconstruction integral comes from the vicinity of the point <math>t,\omega</math> satisfying the phase stationarity condition
For signals having magnitude spectra,
<math>M(t,\omega)</math>, whose time variation is slow
relative to the phase variation, the maximum contribution to
the reconstruction integral comes from the vicinity of the
point <math>t,\omega</math> satisfying the phase
stationarity condition
 
<center>:<math>\begin{matrixalign}
\frac{\partial}{\partial \omega} \left[ \phi_{\tau}(\omega) - \omega \tau + \omega t\right] & = 0 \\
\frac{\partial}{\partial \tau} \left[ \phi_{\tau}(\omega) - \omega \tau + \omega t \right] & = 0
\end{matrixalign}</math></center>
 
or equivalently, around the point <math>\hat{t}, \hat{\omega}</math> defined by
 
<center>:<math>\begin{align}
\hat{t}(\tau, \omega) & = \tau - \frac{\partial \phi_{\tau}(\omega)}{\partial \omega} = -\frac{\partial \phi(\tau, \omega)}{\partial \omega} \\
-\hat{\omega}(\tau, \omega) & = \frac{\partial \phi_{\tau}(\omega)}{\partial \tau} = \omega + \frac{\partial \phi(\tau, \omega)}{\partial \omegatau} \\
\end{align}</math></center>
\hat{\omega}(\tau, \omega) & = \frac{\partial \phi_{\tau}(\omega)}{\partial \tau} =
\omega + \frac{\partial \phi(\tau, \omega)}{\partial \tau} .
\end{align}</math></center>
 
This phenomenon is known in such fields as optics as the [[stationary phase approximation|principle of stationary phase]], which states that for periodic or quasi-periodic signals, the variation of the Fourier phase spectrum not attributable to periodic oscillation is slow with respect to time in the vicinity of the frequency of oscillation, and in surrounding regions the variation is relatively rapid. Analogously, for impulsive signals, that are concentrated in time, the variation of the phase spectrum is slow with respect to frequency near the time of the impulse, and in surrounding regions the variation is relatively rapid.
This phenomenon is known in such fields as optics as the
[[stationary phase approximation|principle of stationary phase]],
which states that for periodic or quasi-periodic
signals, the variation of the Fourier phase spectrum not
attributable to periodic oscillation is slow with respect to
time in the vicinity of the frequency of oscillation, and in
surrounding regions the variation is relatively rapid.
Analogously, for impulsive signals, that are concentrated in
time, the variation of the phase spectrum is slow with
respect to frequency near the time of the impulse, and in
surrounding regions the variation is relatively rapid.
 
In reconstruction, positive and negative contributions to the synthesized waveform cancel, due to destructive interference, in frequency regions of rapid phase variation. Only regions of slow phase variation (stationary phase) will contribute significantly to the reconstruction, and the maximum contribution (center of gravity) occurs at the point where the phase is changing most slowly with respect to time and frequency.
In reconstruction, positive and negative contributions to
the synthesized waveform cancel, due to destructive
interference, in frequency regions of rapid phase variation.
Only regions of slow phase variation (stationary phase) will
contribute significantly to the reconstruction, and the
maximum contribution (center of gravity) occurs at the point
where the phase is changing most slowly with respect to time
and frequency.
 
The time-frequency coordinates thus computed are equal to the local group delay, <math>\hat{t}_{g}(t,\omega),</math> and local instantaneous frequency, <math>\hat{\omega}_{i}(t,\omega),</math> and are computed from the phase of the short-time Fourier transform, which is normally ignored when constructing the spectrogram. These quantities are ''local'' in the sense that they represent a windowed and filtered signal that is localized in time and frequency, and are not global properties of the signal under analysis.
The time-frequency coordinates thus computed are equal to
the local group delay, <math>\hat{t}_{g}(t,\omega)</math>,
and local instantaneous frequency, <math>\hat{\omega}
_{i}(t,\omega)</math>, and are computed from the phase of
the short-time Fourier transform, which is normally ignored
when constructing the spectrogram. These quantities are
''local'' in the sense that they represent a windowed
and filtered signal that is localized in time and frequency,
and are not global properties of the signal under analysis.
 
The modified moving window method, or method of reassignment, changes (reassigns) the point of attribution of <math>\epsilon(t,\omega)</math> to this point of maximum contribution <math>\hat{t}(t,\omega), \hat{\omega}(t,\omega)</math>, rather than to the point <math>t,\omega</math> at which it is computed. This point is sometimes called the ''center of gravity'' of the distribution, by way of analogy to a mass distribution. This analogy is a useful reminder that the attribution of spectral energy to the center of gravity of its distribution only makes sense when there is energy to attribute, so the method of reassignment has no meaning at points where the spectrogram is zero-valued.
The modified moving window method, or method of
reassignment, changes (reassigns) the point of attribution
of <math>\epsilon(t,\omega)</math> to this point of maximum
contribution <math>\hat{t}(t,\omega),
\hat{\omega}(t,\omega)</math>, rather than to the point
<math>t,\omega</math> at which it is computed. This point is
sometimes called the ''center of gravity'' of the
distribution, by way of analogy to a mass distribution. This
analogy is a useful reminder that the attribution of
spectral energy to the center of gravity of its distribution
only makes sense when there is energy to attribute, so the
method of reassignment has no meaning at points where the
spectrogram is zero-valued.
 
== Efficient computation of reassigned times and frequencies ==