L26. More on Sound File Processing

Slides:



Advertisements
Similar presentations
STATISTICS.
Advertisements

CS Spring 2009 CS 414 – Multimedia Systems Design Lecture 4 – Digital Image Representation Klara Nahrstedt Spring 2009.
CMPS1371 Introduction to Computing for Engineers PROCESSING SOUNDS.
Physics 1251 The Science and Technology of Musical Sound Unit 1 Session 8 Harmonic Series Unit 1 Session 8 Harmonic Series.
EE Audio Signals and Systems Psychoacoustics (Pitch) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Maths for Computer Graphics
Chapter 13 Sounds and signals basics of computer sound perception and generation of sound synthesizing complex sounds sampling sound signals simple example.
Dual Tone Multi-Frequency System Michael Odion Okosun Farhan Mahmood Benjamin Boateng Project Participants: Dial PulseDTMF.
Examining the Signal Examine the signal using a very high-speed system, for example, a 50 MHz digital oscilloscope.
Digital Signal Processing A Merger of Mathematics and Machines 2002 Summer Youth Program Electrical and Computer Engineering Michigan Technological University.
Lab3 (Signal & System) Instructor: Anan Osothsilp Date: 13 Feb 07.
Signals, Fourier Series
Insight Through Computing 25. Working with Sound Files Cont’d Frequency Computations Touchtone Phones.
CELLULAR COMMUNICATIONS DSP Intro. Signals: quantization and sampling.
EE Audio Signals and Systems Psychoacoustics (Masking) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Frequency Domain Representation of Sinusoids: Continuous Time Consider a sinusoid in continuous time: Frequency Domain Representation: magnitude phase.
EE513 Audio Signals and Systems Digital Signal Processing (Systems) Kevin D. Donohue Electrical and Computer Engineering University of Kentucky.
Lecture 1 Signals in the Time and Frequency Domains
Lecture 6: Aliasing Sections 1.6. Key Points Two continuous-time sinusoids having di ff erent frequencies f and f (Hz) may, when sampled at the same sampling.
CSC361/661 Digital Media Spring 2002
Wireless and Mobile Computing Transmission Fundamentals Lecture 2.
L 10 The Tempered Scale, Cents. The Tempered Scale.
09/19/2002 (C) University of Wisconsin 2002, CS 559 Last Time Color Quantization Dithering.
Lecture 7: Sampling Review of 2D Fourier Theory We view f(x,y) as a linear combination of complex exponentials that represent plane waves. F(u,v) describes.
If A and B are both m × n matrices then the sum of A and B, denoted A + B, is a matrix obtained by adding corresponding elements of A and B. add these.
Slide Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley.
Factorial Analysis of Variance
Fourier Analysis of Discrete Time Signals
Signals & Systems Lecture 13: Chapter 3 Spectrum Representation.
Discrete Fourier Transform in 2D – Chapter 14. Discrete Fourier Transform – 1D Forward Inverse M is the length (number of discrete samples)
ELECTRICA L ENGINEERING Principles and Applications SECOND EDITION ALLAN R. HAMBLEY ©2002 Prentice-Hall, Inc. Chapter 6 Frequency Response, Bode Plots,
12/2/2015 Fourier Series - Supplemental Notes A Fourier series is a sum of sine and cosine harmonic functions that approximates a repetitive (periodic)
How to Multiply Two Matrices. Steps for Matrix Multiplication 1.Determine whether the matrices are compatible. 2.Determine the dimensions of the product.
INTRODUCTION TO SIGNALS
Algebra Matrix Operations. Definition Matrix-A rectangular arrangement of numbers in rows and columns Dimensions- number of rows then columns Entries-
Learn: to organize and interpret data in frequency tables to organize and interpret data in frequency tables.
$100 $200 $300 $400 $100 $200 $300 $400 $300 $200 $100 Adding multiples of 10 Add 1 to 2 digit numbers with regrouping Add 3 two digit numbers Ways.
2D Fourier Transform.
Matrix Multiplication The Introduction. Look at the matrix sizes.
Two Least Squares Applications Data Fitting Noise Suppression.
Systems of Equations and Matrices Review of Matrix Properties Mitchell.
2.1 Matrix Operations 2. Matrix Algebra. j -th column i -th row Diagonal entries Diagonal matrix : a square matrix whose nondiagonal entries are zero.
REVIEW Linear Combinations Given vectors and given scalars
Properties and Applications of Matrices
Applications of Multirate Signal Processing
Prime and Composite.
Matrix Multiplication
Matrix Operations Monday, August 06, 2018.
Sampling and Quantization
5Th Class Maths Additon & Subtraction.
Dual Tone Multi Frequency (DTMF)
EE Audio Signals and Systems
Dr. Ameria Eldosoky Discrete mathematics
Math.375 V- Interpolation 3 >Sounds and Fourier< >analysis< Vageli Coutsias.
CS Digital Image Processing Lecture 9. Wavelet Transform
Lecture 14 Digital Filtering of Analog Signals
Net 222: Communications and networks fundamentals (Practical Part)
Sinusoids: continuous time
Net 222: Communications and networks fundamentals (Practical Part)
2. Matrix Algebra 2.1 Matrix Operations.
Chapter 4: Representing sound
Associate Professor (Lecturer)
Area Models A strategy for Multiplication
Lecture 5: Sampling of Continuous-Time Sinusoids Sections 1.6
2 Activity 1: 5 columns Write the multiples of 2 into the table below. What patterns do you notice? Colour the odd numbers yellow and the even numbers.
Activity 1: 5 columns Write the multiples of 3 into the table below. What patterns do you notice? Colour the odd numbers yellow and the even numbers blue.
Activity 1: 5 columns Write the multiples of 9 into the table below. What patterns do you notice? Colour the odd numbers yellow and the even numbers blue.
Today's lecture System Implementation Discrete Time signals generation
Matrix Multiplication Sec. 4.2
Review and Importance CS 111.
Presentation transcript:

L26. More on Sound File Processing Frequency Computations Touchtone Phones

Let’s Understand Frequency

A Sinusoidal Function = the frequency Higher frequency means that y(t) changes more rapidly with time.

Digitize for Graphics % Sample “Rate” n = 200 % Sample times tFinal = 1; t = 0:(1/n):tFinal % Digitized Plot… omega = 8; y = sin(2*pi*omega*t) plot(t,y)

Digitize for Sound % Sample Rate Fs = 32768 % Sample times tFinal = 1; t = 0:(1/Fs):tFinal % Digitized sound… omega = 800; y = sin(2*pi*omega*t) sound(y,Fs)

Equal-Tempered Tuning 2 B 61.74 123.47 246.94 493.88 987.77 1975.53 3 C 65.41 130.81 261.63 523.25 1046.50 2093.01 4 C# 69.30 138.59 277.18 554.37 1108.73 2217.46 5 D 73.42 146.83 293.67 587.33 1174.66 2349.32 6 D# 77.78 155.56 311.13 622.25 1244.51 2489.02 7 E 82.41 164.81 329.63 659.26 1318.51 2637.02 8 F 87.31 174.61 349.23 698.46 1396.91 2793.83 9 F# 92.50 185.00 369.99 739.99 1479.98 2959.95 10 G 98.00 196.00 391.99 783.99 1567.98 3135.96 11 G# 103.83 207.65 415.31 830.61 1661.22 3322.44 12 A 110.00 220.00 440.00 880.00 1760.00 3520.00 Entries are frequencies. Each Column is an Octave. Magic Factor = 2^(1/12). C3 = 261.63, A4 = 440.00

Adding Sinusoids Fs = 32768; tFinal = 1; t = 0:(1/Fs):tFinal; C3 = 261.62; yC3 = sin(2*pi*C3*t); A4 = 440.00; yA4 = sin(2*pi*A4*t) y = (yC3 + yA4)/2; sound(y,Fs)

C3: + = A4:

Application: Touchtone Telephones

By Combining Two Sinusoids Let’s Make a Signal By Combining Two Sinusoids

A Frequency Is Associated With Each Row & Column 697 770 852 941 1209 1336 1477

A Frequency Is Associated With Each Row & Column 697 770 852 941 1209 1336 1477

Two Frequencies Are Associated With Each Button 697 “5”-Button corresponds to (770,1336) 770 852 941 1209 1336 1477

Signal For Button 5: Fs = 32768; tFinal = .25; t = 0:(1/Fs):tFinal; yR = sin(2*pi*770*t); yC = sin(2*pi*1336*t) y = (yR + yC)/2; sound(y,Fs)

Two-Frequency “Fingerprint” Each Button Has Its Own Two-Frequency “Fingerprint”

To Minimize Ambiguity… No frequency is a multiple of another The difference between any two frequencies does not equal any of the frequencies. The sum of any two frequencies does not equal any of the frequencies.

The Sinusoids for Buttons 1,2,3, and 4

What Does the Signal Look Like For a Multi-Digit Call?

Buttons Pushed at Equal Time Intervals “Perfect” Signal Each band matches one of the twelve “fingerprints” Buttons Pushed at Equal Time Intervals

Buttons Pushed at Unequal Time Intervals “Noisy” Signal Each band approximately matches one of the twelve “fingerprints” Buttons Pushed at Unequal Time Intervals

The Segmentation Problem When does a Band Begin? When does a band end? Somewhat like the problem of finding an edge in a digitized picture.

Fourier Analysis Once a band is isolated, we know it is the sum of two sinusoids: What are the two frequencies? Fourier analysis tells you.

Knowing the Two Frequencies Means We KnowThe Button 697 Ah-ah! Button 5 was pushed. 770 852 941 1209 1336 1477