Geol 491: Spectral Analysis tom.wilson@mail.wvu.edu

Introduction to Fourier series and Fourier transforms Fourier said that any single valued function could be reproduced as a sum of sines and cosines 5*sin (24t) Amplitude = 5 Frequency = 4 Hz seconds

We are usually dealing with sampled data 5*sin(24t) Amplitude = 5 Frequency = 4 Hz Sampling rate = 256 samples/second Sampling duration = 1 second seconds

Faithful reproduction of the signal requires adequate sampling If our sample rate isn’t high enough, then the output frequency will be lower than the input,

Where t is the sample rate The Nyquist Frequency The Nyquist frequency is equal to one-half of the sampling frequency. The Nyquist frequency is the highest frequency that can be measured in a signal. Where t is the sample rate Frequencies higher than the Nyquist frequencies will be aliased to lower frequency

The Nyquist Frequency Thus if t = 0.004 seconds, fNy = Where t is the sample rate

Fourier series: a weighted sum of sines and cosines Periodic functions and signals may be expanded into a series of sine and cosine functions Experiment with http://www.falstad.com/fourier/

This applet is fun to play with & educational too.

An octave represents a doubling of the frequency. Try making sounds by combining several harmonics (multiples of the fundamental frequency) An octave represents a doubling of the frequency. 220Hz, 440Hz and 880Hz played together produce a “pleasant sound” Frequencies in the ratio of 3:2 represent a fifth and are also considered pleasant to the ear. 220, 660, 1980etc.

You can also observe how filtering of a broadband waveform will change audible waveform properties. http://www.falstad.com/dfilter/

Fourier series The Fourier series can be expressed more compactly using summation notation You’ve seen from the forgoing example that right angle turns, drops, increases in the value of a function can be simulated using the curvaceous sinusoids.

Try the excel file step2.xls Fourier series Try the excel file step2.xls

This can be done with continuous functions or discrete functions The Fourier Transform A transform takes one function (or signal) in time and turns it into another function (or signal) in frequency This can be done with continuous functions or discrete functions

The Fourier Transform The general problem is to find the coefficients: a0, a1, b1, etc. Take the integral of f(t) from 0 to T (where T is 1/f). Note =2/T What do you get? Looks like an average! We’ll work through this on the board.

Getting the other Fourier coefficients To get the other coefficients consider what happens when you multiply the terms in the series by terms like cos(it) or sin(it).

Now integrate f(t) cos(it) This is just the average of i periods of the cosine

Now integrate f(t) cos(it) Use the identity If i=2 then the a1 term =

And what about the other terms in the series? What does this give us? And what about the other terms in the series?

In general to find the coefficients we do the following and The a’s and b’s are considered the amplitudes of the real and imaginary terms (cosine and sine) defining individual frequency components in a signal

Arbitrary period versus 2 Sometimes you’ll see the Fourier coefficients written as integrals from - to  and

cost is considered Re eit Exponential notation cost is considered Re eit

The Fourier Transform A transform takes one function (or signal) and turns it into another function (or signal) Continuous Fourier Transform:

The Fourier Transform A transform takes one function (or signal) and turns it into another function (or signal) The Discrete Fourier Transform:

http://soundmachine.gooddogie.com/sounds4.htm

Tiger.mp3

Classic view

Windows > vertical control

Get a view of the spectrum Try filtering everything out about 1000 Hz

Note how sounds change

Some useful links http://www.falstad.com/fourier/ Fourier series java applet http://www.jhu.edu/~signals/ Collection of demonstrations about digital signal processing http://www.ni.com/events/tutorials/campus.htm FFT tutorial from National Instruments http://www.cf.ac.uk/psych/CullingJ/dictionary.html Dictionary of DSP terms http://jchemed.chem.wisc.edu/JCEWWW/Features/McadInChem/mcad008/FT4FreeIndDecay.pdf Mathcad tutorial for exploring Fourier transforms of free-induction decay http://lcni.uoregon.edu/fft/fft.ppt This presentation