Sound synthesis with Pure Data

Size: px
Start display at page:

Download "Sound synthesis with Pure Data"

Transcription

1 Sound synthesis with Pure Data 1. Start Pure Data from the programs menu in classroom TC307. You should get the following window: The DSP check box switches sound output on and off. Getting sound out First, we ll set up a simple oscillator to test the sound. Open a new workspace by going to File>New. A blank window should pop up. To create an object in PD, go to Put>Object, or press ctrl+1.

2 A small box will appear. Click to place it on the workspace. WE WILL NOW DEFINE WHAT TYPE OF OBECT THIS SHOULD BE. Type osc~ into the box, then click outside of the box. You will see that the box is now and OSC or oscillator object. Create another object, except this time, set it as a dac~ in the same way as you set the OSC. This block is the digital to analogue convertor, and defines what is sent to your computer soundcard.

3 Connect the output of the oscillator to the DAC. TO do this, click the box in the bottom left of OSC, when a small circle appears, and hold and drag the resulting line to one of the top boxes on the DAC. In general, connections on the bottom are outputs, and the top are inputs. Repeat the process to connect the OSC output to the other DAC input. You should now have something like this: Now we have an oscillator connected to the soundcard output. However, the oscillator has no control signals. Therefore, we need to define the frequency that the oscillator will work at. There are different types of objects in Pure Data. We need to drive the oscillator with a NUMBER input. Go to Put>Number or press Ctrl+3, and place a number object above the oscillator. A box with a 0 will appear. Connect this box to the LEFT input of the oscillator. In Pure Data, where objects have 2 inputs, one of the inputs is live. You can see on the osc~ object above, the left input is black. The object only updates when a signal is changed on the LEFT input.

4 You should now have the following. You are ready to make some noise! To edit the value in the number box, go to the Edit menu and take PD out of Edit Mode, or press Ctrl+E.

5 You can vary the value in the oscillator driving number box. Click the box and type a sensible value, e.g Press return. This is the frequency in Hz at which the oscillator will produce a sine wave. Now switch on Output from Pure Data. First, check that PC volume is set to a sensible level, then click the DSP check box. Clicking the box next to the IN OUT meters will allow monitoring of the output level. This is done in the MAIN PD window, now in the window where you are creating your objects. Great, so we have sound output. Now to make some more interesting sounds. Turn the DSP tick box off.

6 FM Synthesis in Pure Data As we know, FM synthesis creates spectrally complex sounds by modulating the frequency input of a simple oscillator with another oscillator. Draw a simple schematic for an FM synthesiser in the space below: (see lecture notes) What differences do you notice between the oscillator objects in Pure Data and those shown in the typical diagram above? In order to control the amplitude (volume) of our sounds, we need to multiply by some gain factor. Go back to our simple oscillator example. Go back INTO Edit Mode, and delete the connections between our oscillator and the output. Do this by clicking on each connection, and pressing delete.

7 Add a multiplier object between the oscillator and the DAC by creating an object (ctrl+1) and typing *~ to set it. We will use this multiplier to control the signal level going to the DAC. Also create a number object. Connect them as shown: You may notice that some of the connections are thick, and some thin. This is to do with the type of signal they are carrying. Thin connections are control signals, whilst thick connections carry audio signals.

8 Turn the DSP tick box on. In the configuration shown above, there should be no sound coming out. Why? Let s add a label to make things clear. Goto Put>Comment and insert a comment saying amplitude control next to our latest number box. Now, let s vary the gain of the multiplier. Exit Edit Mode. HOLD SHIFT and upwards drag on the amplitude control. Holding shift allows fine grain control of the numbers in number boxes. Keep an eye on the audio level meters in the main window. What happens if the amplitude control goes above 1 or below -1? What happens to the quality of the outputted sound when this happens?

9 We saw how we could multiply two objects in pure data with a *~ object. We can also sum using a +~operator. In FM Synthesis, we use 2 frequencies; the carrier and modulator. Currently, we re driving our output oscillator with a single carrier frequency. TO get FM synthesis, we need to vary (modulate) the frequency with which we re driving the oscillator with. Click and drag up and down on the carrier frequency control number as rapidly as you can, in order to change the pitch up and down. What do you hear? How would you describe the sound? We want to be able to vary the pitch more rapidly. Up to many hundreds of times per second. TO do this we should use an oscillator. Remember that the one way of writing the equation describing the output from an FM synthesiser is: x(n) = A(n)sin [2π(f c + I sin(2πf m n)) n]

10 Which terms refer to the carrier signal? Which refer to the modulator? We need to sum the carrier frequency with a modulating frequency. Set up the following: By entering the terms in the boxes shown. Modulator Frequency and Modulator gain boxes are numbers, whilst osc~and *~100 are objects. Experiment with the effect of varying different parameters. Sometimes, due to the order of control signals, varying one parameter will not have the expected effect until the live input on an object is varied. What is the effect of varying the modulator frequency whilst the modulator gain is set to 0?

11 Effect of modulation frequency: Set the carrier to a nice round number, E.G. 200 Hz. Set modulator gain high E.g. 200, so the effects of the modulator are pronounced. Set the modulator frequency to an integer multiple of the carrier. E.G. 200, 400 or 600Hz if the carrier is 200Hz. How would you describe the sound? Now change the carrier frequency gradually so it is no longer an integer multiple. How does the sound change? Now set the modulator frequency low, E.G 1 to 3 Hz. How would you describe the sound? Do you perceive a complex timbre? Gradually increase the frequency, 1 or 2 Hz at a time. At some point, the quality of the sound should change. At what modulation frequency does this happen, and how would you describe the change?

12 Sound Synthesis with Matlab Now we ll try to model a desired instrument sound in Matlab using FM synthesis, filtering, and an ADSR type envelope. We will look at the frequencies present in the waveform and try to model these with FM, then apply a gain envelope to model the dynamics. Load the oboenote.wav file from the course webpage in Matlab. Samplerate is 16kHz. Use the wavread function to load the note and store into a variable, along with the samplerate of the audio. Listen to the note. soundsc(audiodata, fs); How does the QUALITY (timbre) of the sound change over time? Or does it remain fairly constant? Plot the note against time. If the audio file is samples long, how long is this in seconds? To plot against time, make a vector of the same length as the audio file (in samples), with equally spaced numbers. time=(1:length(audiodata))./fs; %Vector mapping sample number to time figure; plot(time, audiodata); xlabel Time, seconds ;

13 You should see something like the following plot: Draw a rough envelope of the AMPLITUDE of the waveform over time: Now let s look at the spectral content of the note, in order to inform parameters for our FM synthesis. If the samplerate of the audio is 16kHz, what is the Nyquist frequency? (the highest frequency which can be sampled)? Take the Fourier transform of the data. AUDIODATA=fft(audioData); When we take the FFT, of course we get complex valued data. We want to inspect the amplitude of the contribution at each frequency. figure; plot(abs(audiodata));

14 The figure produced has both the real frequencies and complex conjugates present. We want only the first half of the values produced. Create a new variable which has only the first N/2 samples present in AUDIODATA. In much the same way as we plotted our time-domain signal against a vector containing time values, we should plot our Fourier analysis against frequency, not sample number. frequencies=((1:length(audiodata))./length(audiodata))*(fs/2); figure; plot(frequencies, abs(audiodata)); You should get the following plot, showing the magnitude of various frequency components: Use the Datatip tool to inspect the following: What is the frequency of the fundamental? What is the frequency of the next biggest peak?

15 Fill in the following table, FOR THE 5 HIGHEST PEAKS (marked with dots): Component Fundamental (f1) Frequency Multiple of f1 1 Difference from 0 f1 Magnitude 1 compared to f1 Peak 2 Peak3 Peak 4 Peak5

16 Now we should synthesis the 5 biggest frequency components using FM synthesis. Load the script basicfm.m. % ---- Test PM synthesis in Matlab fs = 16000; % sample rate (Hz) fcarr = 1; % carrier freq (Hz) fmod = 1; % modulation freq (Hz) A = 1; % amplitude I = 2; % modulation index: ratio of the peak deviation of modulation to the modulating freq n = 0:1/fs:1; % time vector in seconds % PM synthesize the sound y = A * sin( 2*pi*n.*fcarr + I*sin(2*pi*fmod*n)); % Check the spectrum Y = abs( fft(y))/length(y); plot( Y(1:round(length(Y)/2)), 'k'); title('magnitude spectrum'); xlabel('frequency'); soundsc(y, fs); % Play the sound Modify the values of fcarr (carrier frequency) and fmod (modulation frequency) so that they correspond to the values you entered in the table above. Run the script. Inspect the frequency response of the sound produced. Is it similar to the oboe sound we are trying to synthesise? Inspect the time-domain waveform. How does it vary from the time domain waveform of the oboe sound we are trying to synthesise? Create a gain envelope for the synthesized waveform using the linspace command, and concatenating various linear portions together; E.G. > env=[linspace(0, 1, 1000), linspace(1, 0, 2000)] Creates a vector of values which increase from 0 to 1 over 1000 samples, then fall from 1 to 0 over 2000 samples.

17 Make sure that your gain envelope is the same dimensions as the waveform to which it should be applied, and multiply the two together. How does the note sound? How does it compare to the target sound? How could it be improved?

CS 591 S1 Midterm Exam

CS 591 S1 Midterm Exam Name: CS 591 S1 Midterm Exam Spring 2017 You must complete 3 of problems 1 4, and then problem 5 is mandatory. Each problem is worth 25 points. Please leave blank, or draw an X through, or write Do Not

More information

Fourier Series and Gibbs Phenomenon

Fourier Series and Gibbs Phenomenon Fourier Series and Gibbs Phenomenon University Of Washington, Department of Electrical Engineering This work is produced by The Connexions Project and licensed under the Creative Commons Attribution License

More information

ECEGR Lab #8: Introduction to Simulink

ECEGR Lab #8: Introduction to Simulink Page 1 ECEGR 317 - Lab #8: Introduction to Simulink Objective: By: Joe McMichael This lab is an introduction to Simulink. The student will become familiar with the Help menu, go through a short example,

More information

EEL 4350 Principles of Communication Project 2 Due Tuesday, February 10 at the Beginning of Class

EEL 4350 Principles of Communication Project 2 Due Tuesday, February 10 at the Beginning of Class EEL 4350 Principles of Communication Project 2 Due Tuesday, February 10 at the Beginning of Class Description In this project, MATLAB and Simulink are used to construct a system experiment. The experiment

More information

Memorial University of Newfoundland Faculty of Engineering and Applied Science. Lab Manual

Memorial University of Newfoundland Faculty of Engineering and Applied Science. Lab Manual Memorial University of Newfoundland Faculty of Engineering and Applied Science Engineering 6871 Communication Principles Lab Manual Fall 2014 Lab 1 AMPLITUDE MODULATION Purpose: 1. Learn how to use Matlab

More information

Linear Frequency Modulation (FM) Chirp Signal. Chirp Signal cont. CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis

Linear Frequency Modulation (FM) Chirp Signal. Chirp Signal cont. CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis Linear Frequency Modulation (FM) CMPT 468: Lecture 7 Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 26, 29 Till now we

More information

Additive Synthesis OBJECTIVES BACKGROUND

Additive Synthesis OBJECTIVES BACKGROUND Additive Synthesis SIGNALS & SYSTEMS IN MUSIC CREATED BY P. MEASE, 2011 OBJECTIVES In this lab, you will construct your very first synthesizer using only pure sinusoids! This will give you firsthand experience

More information

ELT COMMUNICATION THEORY

ELT COMMUNICATION THEORY ELT 41307 COMMUNICATION THEORY Matlab Exercise #1 Sampling, Fourier transform, Spectral illustrations, and Linear filtering 1 SAMPLING The modeled signals and systems in this course are mostly analog (continuous

More information

EXPERIMENT 4 INTRODUCTION TO AMPLITUDE MODULATION SUBMITTED BY

EXPERIMENT 4 INTRODUCTION TO AMPLITUDE MODULATION SUBMITTED BY EXPERIMENT 4 INTRODUCTION TO AMPLITUDE MODULATION SUBMITTED BY NAME:. STUDENT ID:.. ROOM: INTRODUCTION TO AMPLITUDE MODULATION Purpose: The objectives of this laboratory are:. To introduce the spectrum

More information

FFT analysis in practice

FFT analysis in practice FFT analysis in practice Perception & Multimedia Computing Lecture 13 Rebecca Fiebrink Lecturer, Department of Computing Goldsmiths, University of London 1 Last Week Review of complex numbers: rectangular

More information

Frequency Division Multiplexing Spring 2011 Lecture #14. Sinusoids and LTI Systems. Periodic Sequences. x[n] = x[n + N]

Frequency Division Multiplexing Spring 2011 Lecture #14. Sinusoids and LTI Systems. Periodic Sequences. x[n] = x[n + N] Frequency Division Multiplexing 6.02 Spring 20 Lecture #4 complex exponentials discrete-time Fourier series spectral coefficients band-limited signals To engineer the sharing of a channel through frequency

More information

Type pwd on Unix did on Windows (followed by Return) at the Octave prompt to see the full path of Octave's working directory.

Type pwd on Unix did on Windows (followed by Return) at the Octave prompt to see the full path of Octave's working directory. MUSC 208 Winter 2014 John Ellinger, Carleton College Lab 2 Octave: Octave Function Files Setup Open /Applications/Octave The Working Directory Type pwd on Unix did on Windows (followed by Return) at the

More information

Experiment # 4. Frequency Modulation

Experiment # 4. Frequency Modulation ECE 416 Fall 2002 Experiment # 4 Frequency Modulation 1 Purpose In Experiment # 3, a modulator and demodulator for AM were designed and built. In this experiment, another widely used modulation technique

More information

L A B 3 : G E N E R A T I N G S I N U S O I D S

L A B 3 : G E N E R A T I N G S I N U S O I D S L A B 3 : G E N E R A T I N G S I N U S O I D S NAME: DATE OF EXPERIMENT: DATE REPORT SUBMITTED: 1/7 1 THEORY DIGITAL SIGNAL PROCESSING LABORATORY 1.1 GENERATION OF DISCRETE TIME SINUSOIDAL SIGNALS IN

More information

UNIVERSITY OF WARWICK

UNIVERSITY OF WARWICK UNIVERSITY OF WARWICK School of Engineering ES905 MSc Signal Processing Module (2004) ASSIGNMENT 1 In this assignment, you will use the MATLAB package. In Part (A) you will design some FIR filters and

More information

CMPT 468: Frequency Modulation (FM) Synthesis

CMPT 468: Frequency Modulation (FM) Synthesis CMPT 468: Frequency Modulation (FM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University October 6, 23 Linear Frequency Modulation (FM) Till now we ve seen signals

More information

Problem Set 1 (Solutions are due Mon )

Problem Set 1 (Solutions are due Mon ) ECEN 242 Wireless Electronics for Communication Spring 212 1-23-12 P. Mathys Problem Set 1 (Solutions are due Mon. 1-3-12) 1 Introduction The goals of this problem set are to use Matlab to generate and

More information

Lab 4 Fourier Series and the Gibbs Phenomenon

Lab 4 Fourier Series and the Gibbs Phenomenon Lab 4 Fourier Series and the Gibbs Phenomenon EE 235: Continuous-Time Linear Systems Department of Electrical Engineering University of Washington This work 1 was written by Amittai Axelrod, Jayson Bowen,

More information

8A. ANALYSIS OF COMPLEX SOUNDS. Amplitude, loudness, and decibels

8A. ANALYSIS OF COMPLEX SOUNDS. Amplitude, loudness, and decibels 8A. ANALYSIS OF COMPLEX SOUNDS Amplitude, loudness, and decibels Last week we found that we could synthesize complex sounds with a particular frequency, f, by adding together sine waves from the harmonic

More information

VIBRATO DETECTING ALGORITHM IN REAL TIME. Minhao Zhang, Xinzhao Liu. University of Rochester Department of Electrical and Computer Engineering

VIBRATO DETECTING ALGORITHM IN REAL TIME. Minhao Zhang, Xinzhao Liu. University of Rochester Department of Electrical and Computer Engineering VIBRATO DETECTING ALGORITHM IN REAL TIME Minhao Zhang, Xinzhao Liu University of Rochester Department of Electrical and Computer Engineering ABSTRACT Vibrato is a fundamental expressive attribute in music,

More information

CME 312-Lab Communication Systems Laboratory

CME 312-Lab Communication Systems Laboratory Objective: By the end of this experiment, the student should be able to: 1. Demonstrate the Modulation and Demodulation of the AM. 2. Observe the relation between modulation index and AM signal envelope.

More information

LABORATORY - FREQUENCY ANALYSIS OF DISCRETE-TIME SIGNALS

LABORATORY - FREQUENCY ANALYSIS OF DISCRETE-TIME SIGNALS LABORATORY - FREQUENCY ANALYSIS OF DISCRETE-TIME SIGNALS INTRODUCTION The objective of this lab is to explore many issues involved in sampling and reconstructing signals, including analysis of the frequency

More information

SMS045 - DSP Systems in Practice. Lab 1 - Filter Design and Evaluation in MATLAB Due date: Thursday Nov 13, 2003

SMS045 - DSP Systems in Practice. Lab 1 - Filter Design and Evaluation in MATLAB Due date: Thursday Nov 13, 2003 SMS045 - DSP Systems in Practice Lab 1 - Filter Design and Evaluation in MATLAB Due date: Thursday Nov 13, 2003 Lab Purpose This lab will introduce MATLAB as a tool for designing and evaluating digital

More information

Spectrum. Additive Synthesis. Additive Synthesis Caveat. Music 270a: Modulation

Spectrum. Additive Synthesis. Additive Synthesis Caveat. Music 270a: Modulation Spectrum Music 7a: Modulation Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) October 3, 7 When sinusoids of different frequencies are added together, the

More information

Music 270a: Modulation

Music 270a: Modulation Music 7a: Modulation Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) October 3, 7 Spectrum When sinusoids of different frequencies are added together, the

More information

C.11 Sampling and Aliasing Solution

C.11 Sampling and Aliasing Solution 158 APPENDIX C. LABORATORY EXERCISES SOLUTIONS C.11 Sampling and Aliasing Solution C.11.1 In-lab section 1. To get a frequency sweep from to 12 khz in seconds we need to choose f so that 2ft = when t =.

More information

Advanced Audiovisual Processing Expected Background

Advanced Audiovisual Processing Expected Background Advanced Audiovisual Processing Expected Background As an advanced module, we will not cover introductory topics in lecture. You are expected to already be proficient with all of the following topics,

More information

Discrete Fourier Transform (DFT)

Discrete Fourier Transform (DFT) Amplitude Amplitude Discrete Fourier Transform (DFT) DFT transforms the time domain signal samples to the frequency domain components. DFT Signal Spectrum Time Frequency DFT is often used to do frequency

More information

THE HONG KONG POLYTECHNIC UNIVERSITY Department of Electronic and Information Engineering. EIE2106 Signal and System Analysis Lab 2 Fourier series

THE HONG KONG POLYTECHNIC UNIVERSITY Department of Electronic and Information Engineering. EIE2106 Signal and System Analysis Lab 2 Fourier series THE HONG KONG POLYTECHNIC UNIVERSITY Department of Electronic and Information Engineering EIE2106 Signal and System Analysis Lab 2 Fourier series 1. Objective The goal of this laboratory exercise is to

More information

Signal Processing. Introduction

Signal Processing. Introduction Signal Processing 0 Introduction One of the premiere uses of MATLAB is in the analysis of signal processing and control systems. In this chapter we consider signal processing. The final chapter of the

More information

G(f ) = g(t) dt. e i2πft. = cos(2πf t) + i sin(2πf t)

G(f ) = g(t) dt. e i2πft. = cos(2πf t) + i sin(2πf t) Fourier Transforms Fourier s idea that periodic functions can be represented by an infinite series of sines and cosines with discrete frequencies which are integer multiples of a fundamental frequency

More information

Lecture 3 Complex Exponential Signals

Lecture 3 Complex Exponential Signals Lecture 3 Complex Exponential Signals Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/1 1 Review of Complex Numbers Using Euler s famous formula for the complex exponential The

More information

m208w2014 Six Basic Properties of Sound

m208w2014 Six Basic Properties of Sound MUSC 208 Winter 2014 John Ellinger Carleton College Six Basic Properties of Sound Sound waves create pressure differences in the air. These pressure differences are analogous to ripples that appear when

More information

Experiment One: Generating Frequency Modulation (FM) Using Voltage Controlled Oscillator (VCO)

Experiment One: Generating Frequency Modulation (FM) Using Voltage Controlled Oscillator (VCO) Experiment One: Generating Frequency Modulation (FM) Using Voltage Controlled Oscillator (VCO) Modified from original TIMS Manual experiment by Mr. Faisel Tubbal. Objectives 1) Learn about VCO and how

More information

Physics. AC Circuits ID: 9525

Physics. AC Circuits ID: 9525 AC Circuits ID: 9525 Time required 45 minutes Activity Overview In this activity, students explore a model of alternating electric current. They observe the effects of varying voltage, angular velocity,

More information

EGR 111 Audio Processing

EGR 111 Audio Processing EGR 111 Audio Processing This lab shows how to load, play, create, and filter sounds and music with MATLAB. Resources (available on course website): speech1.wav, birds_jet_noise.wav New MATLAB commands:

More information

ME 365 EXPERIMENT 8 FREQUENCY ANALYSIS

ME 365 EXPERIMENT 8 FREQUENCY ANALYSIS ME 365 EXPERIMENT 8 FREQUENCY ANALYSIS Objectives: There are two goals in this laboratory exercise. The first is to reinforce the Fourier series analysis you have done in the lecture portion of this course.

More information

ECE 2713 Homework 7 DUE: 05/1/2018, 11:59 PM

ECE 2713 Homework 7 DUE: 05/1/2018, 11:59 PM Spring 2018 What to Turn In: ECE 2713 Homework 7 DUE: 05/1/2018, 11:59 PM Dr. Havlicek Submit your solution for this assignment electronically on Canvas by uploading a file to ECE-2713-001 > Assignments

More information

Physics 115 Lecture 13. Fourier Analysis February 22, 2018

Physics 115 Lecture 13. Fourier Analysis February 22, 2018 Physics 115 Lecture 13 Fourier Analysis February 22, 2018 1 A simple waveform: Fourier Synthesis FOURIER SYNTHESIS is the summing of simple waveforms to create complex waveforms. Musical instruments typically

More information

PHYC 500: Introduction to LabView. Exercise 9 (v 1.1) Spectral content of waveforms. M.P. Hasselbeck, University of New Mexico

PHYC 500: Introduction to LabView. Exercise 9 (v 1.1) Spectral content of waveforms. M.P. Hasselbeck, University of New Mexico PHYC 500: Introduction to LabView M.P. Hasselbeck, University of New Mexico Exercise 9 (v 1.1) Spectral content of waveforms This exercise provides additional experience with the Waveform palette, along

More information

Digital Signal Processing ETI

Digital Signal Processing ETI 2012 Digital Signal Processing ETI265 2012 Introduction In the course we have 2 laboratory works for 2012. Each laboratory work is a 3 hours lesson. We will use MATLAB for illustrate some features in digital

More information

Laboratory Assignment 4. Fourier Sound Synthesis

Laboratory Assignment 4. Fourier Sound Synthesis Laboratory Assignment 4 Fourier Sound Synthesis PURPOSE This lab investigates how to use a computer to evaluate the Fourier series for periodic signals and to synthesize audio signals from Fourier series

More information

Music 171: Amplitude Modulation

Music 171: Amplitude Modulation Music 7: Amplitude Modulation Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego (UCSD) February 7, 9 Adding Sinusoids Recall that adding sinusoids of the same frequency

More information

Scanning Digital Radar Receiver Project Proposal. Ryan Hamor. Project Advisor: Dr. Brian Huggins

Scanning Digital Radar Receiver Project Proposal. Ryan Hamor. Project Advisor: Dr. Brian Huggins Scanning Digital Radar Receiver Project Proposal by Ryan Hamor Project Advisor: Dr. Brian Huggins Bradley University Department of Electrical and Computer Engineering December 8, 2005 Table of Contents

More information

ENSC327 Communication Systems Fall 2011 Assignment #1 Due Wednesday, Sept. 28, 4:00 pm

ENSC327 Communication Systems Fall 2011 Assignment #1 Due Wednesday, Sept. 28, 4:00 pm ENSC327 Communication Systems Fall 2011 Assignment #1 Due Wednesday, Sept. 28, 4:00 pm All problem numbers below refer to those in Haykin & Moher s book. 1. (FT) Problem 2.20. 2. (Convolution) Problem

More information

Experiment 1 Introduction to MATLAB and Simulink

Experiment 1 Introduction to MATLAB and Simulink Experiment 1 Introduction to MATLAB and Simulink INTRODUCTION MATLAB s Simulink is a powerful modeling tool capable of simulating complex digital communications systems under realistic conditions. It includes

More information

A-110 VCO. 1. Introduction. doepfer System A VCO A-110. Module A-110 (VCO) is a voltage-controlled oscillator.

A-110 VCO. 1. Introduction. doepfer System A VCO A-110. Module A-110 (VCO) is a voltage-controlled oscillator. doepfer System A - 100 A-110 1. Introduction SYNC A-110 Module A-110 () is a voltage-controlled oscillator. This s frequency range is about ten octaves. It can produce four waveforms simultaneously: square,

More information

Signal Processing. Naureen Ghani. December 9, 2017

Signal Processing. Naureen Ghani. December 9, 2017 Signal Processing Naureen Ghani December 9, 27 Introduction Signal processing is used to enhance signal components in noisy measurements. It is especially important in analyzing time-series data in neuroscience.

More information

Week 2: Plotting in Matlab APPM 2460

Week 2: Plotting in Matlab APPM 2460 Week 2: Plotting in Matlab APPM 2460 1 Introduction Matlab is great at crunching numbers, and one of the fundamental ways that we understand the output of this number-crunching is through visualization,

More information

SigCal32 User s Guide Version 3.0

SigCal32 User s Guide Version 3.0 SigCal User s Guide . . SigCal32 User s Guide Version 3.0 Copyright 1999 TDT. All rights reserved. No part of this manual may be reproduced or transmitted in any form or by any means, electronic or mechanical,

More information

Lab 1B LabVIEW Filter Signal

Lab 1B LabVIEW Filter Signal Lab 1B LabVIEW Filter Signal Due Thursday, September 12, 2013 Submit Responses to Questions (Hardcopy) Equipment: LabVIEW Setup: Open LabVIEW Skills learned: Create a low- pass filter using LabVIEW and

More information

The Formula for Sinusoidal Signals

The Formula for Sinusoidal Signals The Formula for I The general formula for a sinusoidal signal is x(t) =A cos(2pft + f). I A, f, and f are parameters that characterize the sinusoidal sinal. I A - Amplitude: determines the height of the

More information

Electrical & Computer Engineering Technology

Electrical & Computer Engineering Technology Electrical & Computer Engineering Technology EET 419C Digital Signal Processing Laboratory Experiments by Masood Ejaz Experiment # 1 Quantization of Analog Signals and Calculation of Quantized noise Objective:

More information

Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Fall Semester, Introduction to EECS 2

Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Fall Semester, Introduction to EECS 2 Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Fall Semester, 2006 6.082 Introduction to EECS 2 Lab #2: Time-Frequency Analysis Goal:... 3 Instructions:... 3

More information

PART I: The questions in Part I refer to the aliasing portion of the procedure as outlined in the lab manual.

PART I: The questions in Part I refer to the aliasing portion of the procedure as outlined in the lab manual. Lab. #1 Signal Processing & Spectral Analysis Name: Date: Section / Group: NOTE: To help you correctly answer many of the following questions, it may be useful to actually run the cases outlined in the

More information

UNIVERSITY OF WARWICK

UNIVERSITY OF WARWICK UNIVERSITY OF WARWICK School of Engineering ES905 MSc Signal Processing Module (2010) AM SIGNALS AND FILTERING EXERCISE Deadline: This is NOT for credit. It is best done before the first assignment. You

More information

Biomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar

Biomedical Signals. Signals and Images in Medicine Dr Nabeel Anwar Biomedical Signals Signals and Images in Medicine Dr Nabeel Anwar Noise Removal: Time Domain Techniques 1. Synchronized Averaging (covered in lecture 1) 2. Moving Average Filters (today s topic) 3. Derivative

More information

Lab #2 First Order RC Circuits Week of 27 January 2015

Lab #2 First Order RC Circuits Week of 27 January 2015 ECE214: Electrical Circuits Laboratory Lab #2 First Order RC Circuits Week of 27 January 2015 1 Introduction In this lab you will investigate the magnitude and phase shift that occurs in an RC circuit

More information

14 fasttest. Multitone Audio Analyzer. Multitone and Synchronous FFT Concepts

14 fasttest. Multitone Audio Analyzer. Multitone and Synchronous FFT Concepts Multitone Audio Analyzer The Multitone Audio Analyzer (FASTTEST.AZ2) is an FFT-based analysis program furnished with System Two for use with both analog and digital audio signals. Multitone and Synchronous

More information

Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels

Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels Lab 8. ANALYSIS OF COMPLEX SOUNDS AND SPEECH ANALYSIS Amplitude, loudness, and decibels A complex sound with particular frequency can be analyzed and quantified by its Fourier spectrum: the relative amplitudes

More information

Digital Signal Processing ETI

Digital Signal Processing ETI 2011 Digital Signal Processing ETI265 2011 Introduction In the course we have 2 laboratory works for 2011. Each laboratory work is a 3 hours lesson. We will use MATLAB for illustrate some features in digital

More information

Lab 12 Laboratory 12 Data Acquisition Required Special Equipment: 12.1 Objectives 12.2 Introduction 12.3 A/D basics

Lab 12 Laboratory 12 Data Acquisition Required Special Equipment: 12.1 Objectives 12.2 Introduction 12.3 A/D basics Laboratory 12 Data Acquisition Required Special Equipment: Computer with LabView Software National Instruments USB 6009 Data Acquisition Card 12.1 Objectives This lab demonstrates the basic principals

More information

Multirate Signal Processing Lecture 7, Sampling Gerald Schuller, TU Ilmenau

Multirate Signal Processing Lecture 7, Sampling Gerald Schuller, TU Ilmenau Multirate Signal Processing Lecture 7, Sampling Gerald Schuller, TU Ilmenau (Also see: Lecture ADSP, Slides 06) In discrete, digital signal we use the normalized frequency, T = / f s =: it is without a

More information

CMPT 368: Lecture 4 Amplitude Modulation (AM) Synthesis

CMPT 368: Lecture 4 Amplitude Modulation (AM) Synthesis CMPT 368: Lecture 4 Amplitude Modulation (AM) Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 8, 008 Beat Notes What happens when we add two frequencies

More information

Pitch Detection Algorithms

Pitch Detection Algorithms OpenStax-CNX module: m11714 1 Pitch Detection Algorithms Gareth Middleton This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 1.0 Abstract Two algorithms to

More information

Alternative View of Frequency Modulation

Alternative View of Frequency Modulation Alternative View of Frequency Modulation dsauersanjose@aol.com 8/16/8 When a spectrum analysis is done on a FM signal, a odd set of side bands show up. This suggests that the Frequency modulation is a

More information

ESE 150 Lab 04: The Discrete Fourier Transform (DFT)

ESE 150 Lab 04: The Discrete Fourier Transform (DFT) LAB 04 In this lab we will do the following: 1. Use Matlab to perform the Fourier Transform on sampled data in the time domain, converting it to the frequency domain 2. Add two sinewaves together of differing

More information

MUSC 316 Sound & Digital Audio Basics Worksheet

MUSC 316 Sound & Digital Audio Basics Worksheet MUSC 316 Sound & Digital Audio Basics Worksheet updated September 2, 2011 Name: An Aggie does not lie, cheat, or steal, or tolerate those who do. By submitting responses for this test you verify, on your

More information

EECS 216 Winter 2008 Lab 2: FM Detector Part II: In-Lab & Post-Lab Assignment

EECS 216 Winter 2008 Lab 2: FM Detector Part II: In-Lab & Post-Lab Assignment EECS 216 Winter 2008 Lab 2: Part II: In-Lab & Post-Lab Assignment c Kim Winick 2008 1 Background DIGITAL vs. ANALOG communication. Over the past fifty years, there has been a transition from analog to

More information

FM AND BESSEL ZEROS TUTORIAL QUESTIONS using the WAVE ANALYSER without a WAVE ANALYSER...137

FM AND BESSEL ZEROS TUTORIAL QUESTIONS using the WAVE ANALYSER without a WAVE ANALYSER...137 FM AND BESSEL ZEROS PREPARATION... 132 introduction... 132 EXPERIMENT... 133 spectral components... 134 locate the carrier... 134 the method of Bessel zeros... 136 looking for a Bessel zero... 136 using

More information

EECS 307: Lab Handout 2 (FALL 2012)

EECS 307: Lab Handout 2 (FALL 2012) EECS 307: Lab Handout 2 (FALL 2012) I- Audio Transmission of a Single Tone In this part you will modulate a low-frequency audio tone via AM, and transmit it with a carrier also in the audio range. The

More information

1B Paper 6: Communications Handout 2: Analogue Modulation

1B Paper 6: Communications Handout 2: Analogue Modulation 1B Paper 6: Communications Handout : Analogue Modulation Ramji Venkataramanan Signal Processing and Communications Lab Department of Engineering ramji.v@eng.cam.ac.uk Lent Term 16 1 / 3 Modulation Modulation

More information

BASIC SYNTHESIS/AUDIO TERMS

BASIC SYNTHESIS/AUDIO TERMS BASIC SYNTHESIS/AUDIO TERMS Fourier Theory Any wave can be expressed/viewed/understood as a sum of a series of sine waves. As such, any wave can also be created by summing together a series of sine waves.

More information

Sound Synthesis Methods

Sound Synthesis Methods Sound Synthesis Methods Matti Vihola, mvihola@cs.tut.fi 23rd August 2001 1 Objectives The objective of sound synthesis is to create sounds that are Musically interesting Preferably realistic (sounds like

More information

ME scope Application Note 01 The FFT, Leakage, and Windowing

ME scope Application Note 01 The FFT, Leakage, and Windowing INTRODUCTION ME scope Application Note 01 The FFT, Leakage, and Windowing NOTE: The steps in this Application Note can be duplicated using any Package that includes the VES-3600 Advanced Signal Processing

More information

E40M Sound and Music. M. Horowitz, J. Plummer, R. Howe 1

E40M Sound and Music. M. Horowitz, J. Plummer, R. Howe 1 E40M Sound and Music M. Horowitz, J. Plummer, R. Howe 1 LED Cube Project #3 In the next several lectures, we ll study Concepts Coding Light Sound Transforms/equalizers Devices LEDs Analog to digital converters

More information

Lab 4: Using the CODEC

Lab 4: Using the CODEC Lab 4: Using the CODEC ECE 2060 Spring, 2016 Haocheng Zhu Gregory Ochs Monday 12:40 15:40 Date of Experiment: 03/28/16 Date of Submission: 04/08/16 Abstract This lab covers the use of the CODEC that is

More information

Section 10: Radio Frequency Communication

Section 10: Radio Frequency Communication Section 10: Radio Frequency Communication Section Contents This section contains the following: Introducing Radio Frequency on page 10-2 RF Amplifier with Thermal Noise Source on page 10-4. Worksheets

More information

II. LAB. * Open the LabVIEW program (Start > All Programs > National Instruments > LabVIEW 2012 > LabVIEW 2012)

II. LAB. * Open the LabVIEW program (Start > All Programs > National Instruments > LabVIEW 2012 > LabVIEW 2012) II. LAB Software Required: NI LabVIEW 2012, NI LabVIEW 4.3 Modulation Toolkit. Functions and VI (Virtual Instrument) from the LabVIEW software to be used in this lab: niusrp Open Tx Session (VI), niusrp

More information

Introduction to Simulink

Introduction to Simulink EE 460 Introduction to Communication Systems MATLAB Tutorial #3 Introduction to Simulink This tutorial provides an overview of Simulink. It also describes the use of the FFT Scope and the filter design

More information

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday.

Reading: Johnson Ch , Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday. L105/205 Phonetics Scarborough Handout 7 10/18/05 Reading: Johnson Ch.2.3.3-2.3.6, Ch.5.5 (today); Liljencrants & Lindblom; Stevens (Tues) reminder: no class on Thursday Spectral Analysis 1. There are

More information

Experiment 1 Introduction to Simulink

Experiment 1 Introduction to Simulink 1 Experiment 1 Introduction to Simulink 1.1 Objective The objective of Experiment #1 is to familiarize the students with simulation of power electronic circuits in Matlab/Simulink environment. Please follow

More information

THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA. Department of Electrical and Computer Engineering. ELEC 423 Digital Signal Processing

THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA. Department of Electrical and Computer Engineering. ELEC 423 Digital Signal Processing THE CITADEL THE MILITARY COLLEGE OF SOUTH CAROLINA Department of Electrical and Computer Engineering ELEC 423 Digital Signal Processing Project 2 Due date: November 12 th, 2013 I) Introduction In ELEC

More information

ME scope Application Note 02 Waveform Integration & Differentiation

ME scope Application Note 02 Waveform Integration & Differentiation ME scope Application Note 02 Waveform Integration & Differentiation The steps in this Application Note can be duplicated using any ME scope Package that includes the VES-3600 Advanced Signal Processing

More information

DFT: Discrete Fourier Transform & Linear Signal Processing

DFT: Discrete Fourier Transform & Linear Signal Processing DFT: Discrete Fourier Transform & Linear Signal Processing 2 nd Year Electronics Lab IMPERIAL COLLEGE LONDON Table of Contents Equipment... 2 Aims... 2 Objectives... 2 Recommended Textbooks... 3 Recommended

More information

Synthesis Techniques. Juan P Bello

Synthesis Techniques. Juan P Bello Synthesis Techniques Juan P Bello Synthesis It implies the artificial construction of a complex body by combining its elements. Complex body: acoustic signal (sound) Elements: parameters and/or basic signals

More information

The University of Queensland School of Information Technology and Electrical Engineering. ELEC3004/7312: Signals, Systems and Controls

The University of Queensland School of Information Technology and Electrical Engineering. ELEC3004/7312: Signals, Systems and Controls The University of Queensland School of Information Technology and Electrical Engineering ELEC3004/7312: Signals, Systems and Controls EXPERIMENT 3: ECHO FILTERS ON THE NEXYS 2 Aims In this laboratory session

More information

Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Spring Semester, Introduction to EECS 2

Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Spring Semester, Introduction to EECS 2 Massachusetts Institute of Technology Dept. of Electrical Engineering and Computer Science Spring Semester, 2007 6.082 Introduction to EECS 2 Lab #3: Modulation and Filtering Goal:... 2 Instructions:...

More information

Many powerful new options were added to the MetaSynth instrument architecture in version 5.0.

Many powerful new options were added to the MetaSynth instrument architecture in version 5.0. New Instruments Guide - MetaSynth 5.0 Many powerful new options were added to the MetaSynth instrument architecture in version 5.0. New Feature Summary 11 new multiwaves instrument modes. The new modes

More information

Waveshaping Synthesis. Indexing. Waveshaper. CMPT 468: Waveshaping Synthesis

Waveshaping Synthesis. Indexing. Waveshaper. CMPT 468: Waveshaping Synthesis Waveshaping Synthesis CMPT 468: Waveshaping Synthesis Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University October 8, 23 In waveshaping, it is possible to change the spectrum

More information

University of Pennsylvania Department of Electrical and Systems Engineering Digital Audio Basics

University of Pennsylvania Department of Electrical and Systems Engineering Digital Audio Basics University of Pennsylvania Department of Electrical and Systems Engineering Digital Audio Basics ESE250 Spring 2013 Lab 4: Time and Frequency Representation Friday, February 1, 2013 For Lab Session: Thursday,

More information

6.02 Practice Problems: Modulation & Demodulation

6.02 Practice Problems: Modulation & Demodulation 1 of 12 6.02 Practice Problems: Modulation & Demodulation Problem 1. Here's our "standard" modulation-demodulation system diagram: at the transmitter, signal x[n] is modulated by signal mod[n] and the

More information

Lab 1: Analog Modulations

Lab 1: Analog Modulations Lab 1: Analog Modulations Due: October 11, 2018 This lab contains two parts: for the first part you will perform simulation entirely in MATLAB, for the second part you will use a hardware device to interface

More information

Sound Synthesis. A review of some techniques. Synthesis

Sound Synthesis. A review of some techniques. Synthesis Sound Synthesis A review of some techniques Synthesis Synthesis is the name given to a number of techniques for creating new sounds. Early synthesizers used electronic circuits to create sounds. Modern

More information

Laboratory Assignment 2 Signal Sampling, Manipulation, and Playback

Laboratory Assignment 2 Signal Sampling, Manipulation, and Playback Laboratory Assignment 2 Signal Sampling, Manipulation, and Playback PURPOSE This lab will introduce you to the laboratory equipment and the software that allows you to link your computer to the hardware.

More information

Lab 4 Digital Scope and Spectrum Analyzer

Lab 4 Digital Scope and Spectrum Analyzer Lab 4 Digital Scope and Spectrum Analyzer Page 4.1 Lab 4 Digital Scope and Spectrum Analyzer Goals Review Starter files Interface a microphone and record sounds, Design and implement an analog HPF, LPF

More information

SuperCollider Tutorial

SuperCollider Tutorial SuperCollider Tutorial Chapter 6 By Celeste Hutchins 2005 www.celesteh.com Creative Commons License: Attribution Only Additive Synthesis Additive synthesis is the addition of sine tones, usually in a harmonic

More information

Laboratory Experiment #1 Introduction to Spectral Analysis

Laboratory Experiment #1 Introduction to Spectral Analysis J.B.Francis College of Engineering Mechanical Engineering Department 22-403 Laboratory Experiment #1 Introduction to Spectral Analysis Introduction The quantification of electrical energy can be accomplished

More information

FIR/Convolution. Visulalizing the convolution sum. Convolution

FIR/Convolution. Visulalizing the convolution sum. Convolution FIR/Convolution CMPT 368: Lecture Delay Effects Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University April 2, 27 Since the feedforward coefficient s of the FIR filter are

More information

The Fast Fourier Transform

The Fast Fourier Transform The Fast Fourier Transform Basic FFT Stuff That s s Good to Know Dave Typinski, Radio Jove Meeting, July 2, 2014, NRAO Green Bank Ever wonder how an SDR-14 or Dongle produces the spectra that it does?

More information