introduction to fourier series math 250b, spring 2010 03.02.10
TRANSCRIPT
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Introduction To Fourier Series
Math 250B, Spring 2010
03.02.10
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Blackbird (Turdus merula)
Spectrogram
Time Waveform
time
frequency
amplitude
http://www.birdsongs.it/
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Me
matic
sa
th
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Mathematics: Relationship Between Taylor and Fourier Series
Imagine a periodic time-series (w/ period 2) described by the following function:
Taylor series about t=0
OR
Fourier series for t=[-,]
- Taylor series expands as a linear combination of polynomials
- Fourier series expands as a linear combination of sinusoids
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Trigonometry review Sinusoids (e.g. tones)
A sinusoid has 3 basic properties:i. Amplitude - height of waveii. Frequency = 1/T [Hz]iii. Phase - tells you where the
peak is (needs a reference)
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Why Use Fourier Series?
2. Taylor series can give a good local approximation (given you are within the radius of convergence); Fourier series give good global approximations
1. Many phenomena in nature repeat themselves (e.g., heartbeat, songbird singing)
Might make sense to ‘approximate them by periodic functions’
4. Fourier series gives us a means to transform from the time domain to frequency domain and vice versa (e.g., via the FFT)
Can be easier to see things in one domain as opposed to another
3. Still works even if f (t) is not periodic
0. Idea put forth by Joseph Fourier (early 19’th century); his thesis committee was not impressed [though Fourier methods have revolutionized many fields of science and engineering]
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time waveform recorded from ear canal
... zoomed in
Fourier transform
Time Domain Spectral Domain
One of the ear’s primary functions is to act as a Fourier ‘transformer’
Tone-like sounds spontaneously emitted by the ear
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Example: Square Wave
For periodic function f with period b, Fourier series on t =[-b/2, b/2] is:
where
(these are called the Fourier coefficients)
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Example: Square Wave (cont.)
When the smoke clears....
include first two terms only (red)
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include first three terms only (black dashed)
include first four terms only (green)
Note that approximation gets better as the number of higher order terms included increases
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SUMMARY
- Taylor series expands as a linear combination of polynomials
- Fourier series expands as a linear combination of sinusoids
- Idea is that a function (or a time waveform) can effectively be represented as a linear combination of basis functions, which can be very useful in a number of different practical contexts
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Fini
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NOTE: different vertical scales! (one is logarithmic)
Why might the ear emit sound? An Issue of Scale
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decibels (dB)
0 dB = x1
10 dB x3
20 dB = x10
40 dB = x100
60 dB = x1000
80 dB = x10000
100 dB = x100000
…
• a dB value is a comparison of two numbers
[dB= 20 log(x/y)] • A means to manage
numbers efficiently But why do we need
to use a dB scale?
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Humans hear over a pressure range of 120 dB
Dynamic Range
[that’s a factor of a million]
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‘The ear is capable of processing soundsover a remarkably wide intensity range, encompassing at least a million-fold change in energy….’ - Peter Dallos
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VS
x5
Energy is related to the square of pressure …
WRONG ANALOGY
‘To appreciate this range … we represent a similar range of potential energies by contrasting the weight of a mouse with that of five elephants.’
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VS
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human threshold curve
SOAEs byproduct of an amplification mechanism?
SOAEs & Threshold
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Mathematical Model: coupled resonators (2nd order filters)
Model Schematic
Each resonator has a unique tuning bandwidth [Q(x)] and spatially-defined characteristic frequency [(x)]
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Equation of Motion
Assumptions-inner fluids are incompressible and the pressure is uniform within each scalae-papilla moves transversely as a rigid body (rotational modes are ignored)- consider hair cells grouped together via a sallet, each as a resonant element (referred to as a bundle from here on out) - bundles are coupled only by motion of papilla (fluid coupling ignored) - papilla is driven by a sinusoidal force (at angular frequency )- system is linear and passive- small degree of irregularity is manifest in tuning along papilla length
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An Emission Defined
[SFOAE is complex difference between ‘smooth’ and ‘rough’ conditions]
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Phase-Gradient Delay
Analytic Approximation
-To derive an approximate expression for the model phase-gradient delay, we make several simplifying assumptions (e.g., convert sum to integral, assuming bundle stiffness term is approximately constant, etc.)
given the strongly peaked nature of the integrand and by analogy to coherent reflection theory, we expect that only spatial frequencies close to some optimal value will contribute
opt
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Analytic Approximation (cont.)
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Model and Data Comparison
Model can be used to help us better understand physiological processes at work in the ear giving rise to emissions, leading to new science and clinical applications
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Bundle = Force Generator?
- bundle can oscillate spontaneously
- exhibits nonlinear and negative stiffness
Martin (2008)
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Simple Model to Explain SOAEs?: Part I
- ear is composed of resonant filters (e.g. a second-order filter such as a harmonic oscillator)
e.g., an individual hair cell or a particular location along the length of the basilar membrane
- consider just one of these filters:
massterm
dampingterm
stiffnessterm
drivingterm
Frishkopf & DeRosier (1983)
NOTE: quantities are complex so to describe both magnitude and phase
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http://en.wikipedia.org/wiki/Image:VanDerPolOscillator.png
- active term?
- nonlinear?
negative damping
yup!
Model Idea I: SOAEs arise due to self-sustained oscillations of individual resonators (e.g. a limit cycle)
- one can readily envision adding in a driving term (e.g. stochastic force due to thermal noise)
- need some sort of ‘active’ term for self-sustained oscillation (e.g. van der Pol)
Simple Model to Explain SOAEs?: Part I (cont.)
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Hair cell = ‘mechano-electro’ transducer
1. Mechanical stimulation deflects bundle, opening transduction channels
2. HC membrane depolarizes
3. Vesicle release triggers synapsed neuron to fire
non-linear (saturation)
+80 mV
-60 mV
Martin (2008)