the story of wavelets theory and engineering applications
DESCRIPTION
The Story of Wavelets Theory and Engineering Applications. Time frequency representation Instantaneous frequency and group delay Short time Fourier transform –Analysis Short time Fourier transform – Synthesis Discrete time STFT. Time – Frequency Representation. Why do we need it? - PowerPoint PPT PresentationTRANSCRIPT
The Story of WaveletsTheory and Engineering Applications
• Time frequency representation
• Instantaneous frequency and group delay
• Short time Fourier transform –Analysis
• Short time Fourier transform – Synthesis
• Discrete time STFT
Time – Frequency Representation
Why do we need it?Time info difficult to interpret in frequency
domainFrequency info difficult to interpret in time
domainPerfect time info in time domain , perfect freq.
info in freq. domain …Why?How to handle non-stationary signals
Instantaneous frequency Group Delay
Instantaneous Frequency & Group Delay
Instantaneous frequency: defined as the rate of change in phase
A dual quantity group delay defined as the rate of change in phase spectrum
)(2
1)( tx
dt
dtfx
)(2
1)( fX
df
dftx
Frequency as a function of time
Time as a function of frequency
What is wrong with these quantities???
Time Frequency Representation in Two-dimensional Space
TFR
LinearSTFT, WT, etc.
QuadraticSpectrogram, WD
Non-Linear
STFT
….. …..
time
Am
plit
ude
Fre
quen
cy …..…..
t0 t1 tk tk+1 tn
The Short Time Fourier Transform
Take FT of segmented consecutive pieces of a signal. Each FT then provides the spectral content of that time
segment onlySpectral content for different time intervalsTime-frequency representation
t
tjx dtetWtxSTFT )()(),(
STFT of signal x(t):Computed for each window centered at t=(localized spectrum)
Time parameter Frequency
parameter
Signal to be analyzed
Windowingfunction
(Analysis window)Windowing function
centered at t=
FT Kernel(basis function)
Properties of STFT
Linear Complex valued Time invariant Time shift Frequency shift Many other properties of the FT also apply.
Alternate Representation of STFT
deXetSTFT
dfefffXeftSTFT
tjtjx
ftj
f
tfjx
)~()()~,(
)~
()()~
,(
*~)(
2*~
2)(
STFT : The inverse FT of the windowed spectrum, with a phase factor
)~
()( * fffX
Filter Interpretation of STFT
ftj
ftj
f
ettxfffX
dfefffXfffXF
2*
2**1
)()()~
()(
)~
()()~
()(
X(t) is passed through a bandpass filter with a center frequency of Note that (f) itself is a lowpass filter.
f~
Filter Interpretation of STFT
x(t) ftjet 2)( X
ftje 2
),()( ftSTFTx
x(t) )( t ),()( ftSTFTxX
ftje 2
Resolution Issues
time
Am
plit
ude
Fre
quen
cy
k
All signal attributes located within the local window intervalaround “t” will appear at “t” in the STFT
)( kt
n
)( kt
Time-Frequency Resolution
Closely related to the choice of analysis windowNarrow window good time resolutionWide window (narrow band) good frequency
resolution Two extreme cases:
(T)=(t) excellent time resolution, no frequency resolution
(T)=1 excellent freq. resolution (FT), no time info!!!How to choose the window length?
Window length defines the time and frequency resolutions Heisenberg’s inequality
Cannot have arbitrarily good time and frequency resolutions. One must trade one for the other. Their product is bounded from below.
Time-Frequency Resolution
Time
Fre
quen
cy
Time Frequency Signal Expansion and STFT Synthesis
f
tfjx fddetgfSTFTtx
~
~2)( ~
)()~
,()(
Synthesis window
Basis functions
Coefficients (weights)
Synthesized signal
• Each (2D) point on the STFT plane shows how strongly a time frequency point (t,f) contributes to the signal. • Typically, analysis and synthesis windows are chosen to be identical.
1)(*)( dtttg
300 Hz 200 Hz 100Hz 50Hz
STFT Example
2/2
)( atet
STFT Example
a=0.01
STFT Example
a=0.001
STFT Example
a=0.0001
STFT Example
a=0.00001
STFT Example
Discrete Time Stft
n k
kFtjx enTtgkFnTSTFTtx 2)( )(),()(
dtenTttxkFnTSTFT kFtj
tx
2*)( )()(),(