Goal: Analyze a Wave File Generating and Saving WAV files Sound

Analyze a Wave File
Generating and Saving WAV files
Sound Recorder; Volume Controls (watch out for mute);
Play back to test quality…
Setting up folders
Work in pairs; 1 microphone
Record all vowels for each person
Suggested filenames: fred_a.wav in folder named fred
Math involved here:
Sampling rate, duration => filesize
Should be able to predict file size
3.5 seconds, 22,050 samples per sec, mon0 => what size?
3.5 x 22,050 = 77175 bytes
Fourier Series
Creation of periodic functions
Series of sines and cosines or exponentials to “build” signals
Orthogonal building blocks see Table
Sines and Cosines
We want to use components that have nothing in common = orthogonal
Inner product or dot product is zero
Trigonometric Fourier Series
Exponential Fourier Series
Limit of Complex EFS creates Fourier Transform
Frequency Domain (Hertz)
Transform takes us from the time domain to the frequency domain
Fourier Transform Function in Matlab, command fft( )
Notable Frequency Information
CD’s = 44,100Hz Sampling Frequency
Voice recording = 22,050Hz Sampling Frequency
Human Speech < 4,000Hz
FM (Frequency Modulation) High carrier frequencies
Matlab Tutorial with command lines for you to enter in as you proceed
Wave File
Tuning Fork
Matlab (Matrix Laboratory)
Import a wave file
Plot in time domain (seconds as x axis)
Carry out Fast Fourier Transform Algorithm
Plot in frequency domain (Hertz)
Your voice pronouncing vowels, consonants, words
Fundamental frequency is first spike in frequency domain
All other relevant frequency information is a multiple of that frequency
Prove this in Matlab
Voice Recording
Record your own voice onto computer via microphone
Just as completed in the Tutorial, import the file
Plot in time domain
Take FFT
Plot in frequency domain
Possible Applications – Projects
1) voice print identification – can you tell who someone is based upon
their voice?
2) intelligent hearing aid – filter out noise, focus on voice output so as to
improve the quality
3) filters –
4) MP3 vs WAV files