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ETM 4096: Digital Signal Processing Lab Sheets for labs 1-2

FACULTY OF ENGINEERINGMULTIMEDIA UNIVERSITY

LAB SHEET

ETM 4096DIGITAL SIGNAL PROCESSING

TRIMESTER 3 (2010/2011)

DSP1: Introduction to DSP with Matlab

DSP2: Design of a Digital Filter with Matlab (plus Demo on DSP Starter

Kit)

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Introduction to the Digital Signal Processing LabsStudents should develop the habit to read user manuals before using any product (software,

hardware or technical), and that online manuals and/or tutorials can easily be found with the use of any search engine.

The purpose of the "Digital Signal Processing" labs is not to spend a lot of time just working out theoretical results or following blindly some very specific instructions. The purpose is to give some hands-on experience to the student by letting him "play around" with a number of basic signal processing routines, and so develop a certain intuition about digital signal processing.

The expected result is that the student understands what he/she is doing and why he gets the results that he gets. Hence, the report will have include the results, but most of all the discussion of the results (evaluation of the success of the used techniques). Reports have to be brief, to the point, including all the results plus a discussion of the results. Rambling will result in a loss of marks.

Copying lab reports is considered a serious offense!

To reduce the likelihood of copying, each student will be assigned a different audio file to work on (instructions on getting your files will be announced in MMLS). In addition to hard-copy submission, reports will also have to be submitted in floppy disk (returnable) – allowing for "computer-aided copy detection". Please do not send the soft-copy report via email. The student who copies someone else's report and the one who lets somebody else copy his report will both get half of the marks that the report deserves. A complete database of your seniors’ reports is also available for copy detection. Plagiarizing reports of previous years will result in getting zero mark.

Please read the following carefully, failing to comply with any item will result in 20% reduction for each mistake committed:

In case the report is too big to be saved into a floppy disk, it may be compressed using ZIP. The filename of the report should be the student ID number followed by the letter a or b, corresponding to lab 1 or 2 (e.g. the first lab report of student with ID number 95100107 should have the name "95100107a.doc").

For Experiment 2, the student will also have to submit two .wav files : one for the noisy signal filtered using the designed FIR filter, and the other for the noisy signal filtered using the designed IIR filter. The filename of the .wav files be the student ID number followed by the letter i or f, corresponding to the IIR or the FIR filter (e.g. the sound file of the signal filtered with the IIR filter of student with ID number 95100107 should have the name "95100107i.wav").

Email attachments will not be accepted.

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DSP1: Introduction to DSP with Matlab1. Objectives

To get familiar with the basics of Matlab programming environment.

To perform basic signal and filter analysis by using interactive GUI tools of the Signal Processing Toolbox.

2. Equipment

Desktop PC

Matlab 5.3 with Signal Processing Toolbox

3. Background Theory

Introduction to MatlabAs a preparation for your DSP Lab session, start Matlab on any computer that has Matlab installed, type "helpdesk", click on "Getting Started", and read at least following topics:

IntroductionWhat Is MATLAB? The MATLAB System

Getting StartedStarting MATLAB Matrices and Magic Squares

Entering Matrices sum, transpose, and diag Subscripts The Colon Operator The magic Function

Expressions Variables Numbers Operators Functions Expressions

Working with Matrices Generating Matrices load M-Files Concatenation Deleting Rows and Columns

The Command Window

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The format Command Suppressing Output Long Command Lines Command Line Editing

Graphics Creating a Plot Figure Windows Adding Plots to an Existing Graph Subplots Imaginary and Complex Data Controlling Axes Axis Labels and Titles Printing Graphics

Help and Online Documentation The help Command The Help Window The lookfor Command The Help Desk The doc Command Printing Online Reference Pages Link to the MathWorks

The MATLAB Environment The Workspace save Commands

More About Matrices and Arrays Linear Algebra Arrays Scalar Expansion

Flow Control if switch and case for while break

Scripts and FunctionsScripts Functions Vectorization Function Functions

Complementary information is found in the "Matlab Overview 1&2", which will be put online.

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Introduction to Matlab for DSPMatlab can have several "toolboxes" added to it for specific applications. The list of the toolboxes installed can be obtained by typing the "help" command. If the Digital Signal Processing toolbox is installed, the user will see the following three lines in the list:signal\signal - Signal Processing Toolbox.signal\siggui - Signal Processing Toolbox GUIsignal\sigdemos - Signal Processing Toolbox Demonstrations

To obtain the list of functions available under each, the user can type "help signal", "help siggui" and "help sigdemos", respectively.

The basic signals used often in digital signal processing are the unit impulse signal [n], exponentials of the form anu[n], sine waves, and their generalizations to complex exponentials. Since the only numerical data type in MATLAB is the M x N matrix, signals must be represented as vectors: either M x 1 matrices if column vector, or 1 x N matrices if row vectors. In MATLAB all signals must be finite in length. This contrasts sharply with analytical problem solving, where a mathematical formula can be used to represent an infinite-length signal (e.g. a decaying exponential, anu[n]).

A second issue is the indexing domain associated with a signal vector. MATLAB assumes by defauls that a vector is indexed from 1 to N, the vector length. In contrast, a signal vector is often the result of sampling a signal over some domain where the indexing runs from 0 to N - 1; or, perhaps, the sampling starts at some arbitrary index that is negative, e.g. at -N. The information about the sampling domain cannot be attached to the signal vector containing the signal values. Instead, the user is forced to keep track of this information separately. Usually, this is not a problem until it comes time to plot the signal, in which case the horizontal axis must be labeled properly.

A final point is the use of MATLAB's vector notation to generate signals. A significant power of the MATLAB environment is its high-level notation for vector manipulation. for loops are almost always unnecessary. When creating signals such as a sine wave, it is best to apply the sin function to a vector argument, consisting of all the time samples.

To familiarize himself with DSP using Matlab, for the first lab, the student will have to examine a number of demonstration packages and comment on them. As mentioned before, the understanding of what is going on is of the utmost importance, and the report should be individual. Students can discuss between themselves, ask advice and exchange ideas, but each one must describe the pertaining phenomena by himself.

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Digital filters come in two types: FIR (Finite Impulse Response, non-recursive, always linear phase) and IIR (Infinite Impulse Response, recursive, better performance, sometimes unstable). They are low-pass, high-pass, band-pass or band-stop filters, meaning that they let either the low, or the high, or a band of frequency pass, or they stop a band of frequencies. To design a filter, one has to specify the passband (the frequencies that are allowed to pass, from 0 to the edge frequency, or from the edge frequency to the maximum, or between two frequencies) with the maximum attenuation that can be allowed in that frequency band; and the stopband with the minimum attenuation that is required for those frequencies.

The response of a filter is determined by the positions of its zeros (for FIR filters) or poles and zeros (for IIR filters) in the imaginary plane. Their position toward the unit circle will be very important, as the unit circle represents the frequencies, from 0 (at complex number 1) to (at complex number -1). To be stable, all the poles should be inside the unit circle. A zero near the unit circle will attenuate the corresponding frequencies, while a pole will boost the same frequencies.

4. Experimental Procedure

The first demonstration package to analyze is filtdem. Type the command "filtdem" in the command window (it may not work for Matlab version 6.0 and above, it’s advisable that you use Matlab version 5.3 when you execute this command). A slideshow starts to illustrate the design of a band pass filter using MATLAB. Examine the slide show attentively. Copy each command of the slideshow into a new m-file, save it and execute it. To open a new M-file, go to File -> New -> M-file. Make it a habit by compiling your programs using a new m-file. If you just type them into the command window and execute them, your codes will be lost after your exit MATLAB.

To have more information about any command, use the help function (e.g. "help ellip") in the command window. The semicolon at the end of the line suppresses the output; to see the output (and observe what the command is doing), type the command without the semicolon. Describe in your report what each command line is doing. Use what you have learned to synthesize a signal that is a combination of 4 sinusoids. Choose frequencies that are all different from the frequencies mentioned in the slideshow and observe the corresponding spectrum. Choose another type of filter (to find the other types of filter, type "help ellip") and analyze the influence of using another filter order. Plot your result using the "stem" command instead of "plot", explain your observations.

The second demonstration package to analyze is filtdemo. Type the command "filtdemo" in the command window. Analyze the various low-pass filter designs using the interactive GUI. Compare the characteristics of the seven available filters, examine their overall frequency response as well as their passband and stopband characteristics. Vary the passband and stopband edge frequencies, as well as the passband and stopband attenuations, either numerically, or by interactive drag-and-drop in the display window. Comment on the results.

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The third demonstration package to analyze is sigdemo1. Type the command "sigdemo1" in the command window. Analyze the various signals (sine, rectangular, sawtooth) using the interactive GUI by varying the amplitude and frequency, and seeing the Fourier representation after applying one of the available windows. Comment on the results. Try to vary the frequency, amplitude, signal shape, and window, and comment on the differences. Illustrate the problem of aliasing using the GUI.

The fourth demonstration package to analyze is sptool. Type the command "sptool" in the command window. In the "Filters" column, click the "View" button, disable the "Magnitude" and "Phase" plots, and enable the "Zeros and Poles" plot. Next, in the previous window, click "New Design", which brings you to a window similar to the one encountered with "filtdemo". Arrange the windows so that you can see the "Filter Viewer" and "Filter Designer" windows simultaneously. Analyze and comment on the positions of poles and zeros for different filter design methods, passband and stopband edge frequencies, and passband and stopband attenuations.

5. Report Writing Guidelines

The lab report shall consist of the following headings. Write clearly and concisely to describe the important elements of your experiment. All the graph and table need to be labeled properly. Describe the data with the proper unit..

i) A header page which mentions student name and ID number, subject, lab number, date of

experiment and date of reportii) Introduction

This section introduces the basic theory underlying the experiment.iii) Objectives

State the specific investigation that you would like to conduct in this experiment.

iv) Procedures Explain the procedure and the methodology used

v) Results and Discussion Results, including methods, m-files (if applicable), figures and plots (can

easily be cut using the EditCopy Figure menu item, and pasted in MS Photo Editor for further manipulation, or directly in MS Word document; for windows, screen captures can be used);

Describe the results obtained in a suitable form such as table and graph. Discuss the finding of the experiment from the result obtained.

vi) Conclusion Derive conclusion based on your findings and results

vii) References

The report must be type written. You are warned that the act of experiment data fabrication, copying

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of other people work and failure to acknowledge the source of your information in the report (plagiarism) are serious offences and if found, the student will be penalized.

Marking Scheme

Assessment Components %1 Acquisition of basic MATLAB programming skills. 202 Correctness in calculations and illustrations. 303 Correctness in relating and explaining the results obtained. 304 Depth of discussion. 20

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DSP2: Design of a Digital Filter with Matlab

1. Objectives

In this experiment, students are required to identify and analyze a digital audio signal with some added noise. When the audio signal is recorded, it is often corrupted by noise. The objective of this experiment is to identify these unknown noise signals to obtain the filter specification and to eliminate them using a suitable digital filter implemented in MATLAB. This involves signal spectrum analysis to produce filter performance specification. Subsequently the student is required to design the filter that meet the specification by determining the filter order and finding the filter coefficients. At the end of this lab the student should gain the practical knowledge to design a FIR and IIR digital filter in Matlab based on the given filter specification.

Note

Students are required to learn Matlab and the basic theory of the FIR and IIR filter before coming to the lab.

2. Apparatus

Desktop PC with soundcards,

Matlab 5.3 with Signal Processing Toolbox,

Original and noise-corrupted audio files (WAV format)*.

* Each student will be assigned a different set of original signal and noisy signal in ".wav" format. The signals will be made available on the web site of the course, and will be named using the student ID appended with the letter 'o' or 'n' for the original and noisy signal, respectively (e.g. the original signal of student with ID number 95100107 would have the name "95100107o.wav").

3. Background Theory

A digital filter implements the difference equation that describes the algorithm to process the time domain signal in order to achieve filtering objectives. The objective of filtering is to remove signal in certain frequency range. The difference equation is implemented either in software on DSP processor or on personal computers. It can also be implemented on hardware, for example in FPGA or custom integrated circuit. The objective of the filter design is to obtain the filter coefficients so that the difference equation of the filter can be implemented. Equation (3.1) shows the standard difference equation for IIR filter (order = max (p,q)) and equation (3.2) shows the standard difference equation for FIR filter of order q. The filter coefficients are given by bk and ak .

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Digital filters can be divided into finite impulse response (FIR) and infinite impulse response (IIR) filter. IIR filter contains a feedback loop in the block diagram, hence the transfer function of an IIR filter contains poles, and perhaps zeros as well. FIR filter, on the other hand, does not have the feedback loop, thus its transfer function consists of only zeros. Both types of filter have its own advantages:

Advantages of FIR filter:

Can have exact linear phase,

FIR filters are realized non-recursively, thus are always stable,

Round-off noise and coefficient quantization errors much less severe,

Arbitrary frequency responses.

Advantages of IIR:

Analog filters can be readily transformed into equivalent IIR digital filters. This is impossible with FIR filters as they have no analog counterpart,

Require less filter coefficients than FIR to achieve similar frequency response,

In many applications, linearity of phase response is not an issue.

Generally there are 8 stages in the design of a digital filter:

1. Specification of the filter requirements 2. Choice of a type of filter (FIR or IIR).3. Determination of the filter order.4. Finding a set of coefficients.5. Implementation.6. Quantization.7. Redesigning if necessary.8. Choosing the filter structure.

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The first step of a digital filter design is the filter specification. The specification is in frequency domain and based on the magnitude response of the filter. This is due to frequency selective operation that the filter performs. The important parameters to be included are described in the following diagram.

Figure 1: Digital Filter Specification

As shown in Figure 1, the parameters of the filter specification are the passband edge frequency fp, the stopband edge frequency fs, maximum attenuation in passband (1/Ap), minimum attenuation in the stopband (1/As) and the transition bandwidth (fs – fp). p define the magnitude of the ripples allowed in the passband. The attenuation is usually expressed in the dB unit. The filter to be designed must meet the criteria in the specification. Normally a low pass filter is designed and the transfer function is modified in order obtain other types of filter such as the high pass filter and the bandpass filter. The focus of this experiment is on stages (a) – (d), using both the FIR and IIR filters.

For IIR filter, the filter order and the set of filter coefficients can be found directly from Matlab functions. The design approach commonly used is to design a prototype analog filter and transform it to an equivalent digital filter using method such as the bilinear transform. This approach makes use of the many establish methods of analog filter design available such as the Butterworth and Chebyshev filter. Figure 2 shows the design flow for an IIR filter using bilinear transform. Take note that the cut-off frequency obtained from the filter specification is a digital frequency where = 2 f/fsampling (f is frequency in Hz) and is the prewarped analog frequency =2/Ts tan( /2), Ts= 1/ fsampling . The analog filter uses as the cut-off frequency.

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Filter 2: IIR filter design using bilinear transform

One common method used for designing the FIR filter is the windowing approach. The idea is to truncate the infinite impulse response h(n) of an ideal low pass filter using a window function such as a rectangle function. This is to produce a linear phase and causal FIR filter. This process however distorts the magnitude response of the ideal filter by introducing ripples in the passband and stopband. In addition, the transition band is created as well. Different types of window such as the Hanning, Blackman and Hamming window can be used to mitigate this unwanted effect. Figure 2 below shows the effect of windowing on the ideal frequency response.

cut-off

Figure 3: The ideal desired frequency response and the practical response

For FIR filter, we need to first determine the window type and the filter order. The window type is determined based on the stopband attenuation, while the filter order is identified based on the transition width:

- Rectangular window: αs= -21dB, N = 0.9 / f;

- Hanning window: αs= -44dB, N = 3.1 / f;

- Hamming window: αs= -53dB, N = 3.3 / f;

- Blackman window: αs= -74dB, N = 5.5 / f;

- Kaiser window: αs= -20log(δs), N = (s - 7.95) / (14.36 f);

f =( fstop - fpass)/ fsampling

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The filter coefficients of FIR filter can then be found using Matlab function.

4. Experimental Procedure

The audio signals need to be compared both in time and in frequency domain. To read the signal, use the function wavread. You can also use the function wavread to identify the sampling frequency of audio files (type ‘help wavread’). The length of the signal can be obtained using the command length. The sound wave can be played using the command wavplay (don't be too annoying for your fellow students by playing it over and over again…). Before the filter specification can be obtained, the frequency spectrum of the clean and noisy signal will need to be obtained. This enable you to locate the noise frequency range by comparing the spectrum plot for both the clean and noisy signal. The frequency spectrum or Fourier transform can be obtained using the command fft. Note that the fft is a complex function, hence it can be split up in its real and imaginary parts (functions imag and real) or in its amplitude and phase (abs and angle). Usually, we are more interested in the amplitude than in the phase - can you see why from the plot of both phase and amplitude? You are only required to plot the spectrum magnitude till the Nyquist frequency.

Plot the amplitude of the frequency spectrum for both the original clean signal and noisy signal. From the comparison between both, specify the requirements for a filter that would eliminate the noise as much as possible and alter the signal as little as possible. From passband edge frequency fp and stopband edge frequency fs, the cutoff frequency fc is calculated as the average of both. The student will have to implement both an FIR and an IIR filter. Although the concepts behind each type of filter are very different, and their usage is also quite different, their design using MATLAB is quite similar, thanks to the power of the MATLAB Signal Processing Toolbox.

For the FIR filter, the command fir1 will be used. For this experiment, you should achieve a peak passband ripple of 3 dB and minimum stopband attenuation of 30 dB. Which window will you choose? Why? Determine the filter order N for the chosen window. Design the FIR filter using the function fir1. The frequency response of the filter can be obtained using the function freqz. Plot the designed filter frequency response and see whether the specifications are met. Check especially the passband and stopband edge frequencies. If necessary, redesign the filter.

Apply the filter to the noisy signal using the function filter. For example, if the noisy signal is stored in vector x and the designed FIR filter coefficients are stored in vector h, we can obtain the filtered signal by using y = filter(h, 1, x). Plot the frequency spectrum or Fourier transform of the filtered signal, and compare it with the Fourier transform of the original and of the noisy signal. Discuss your findings.

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For the IIR filter, the student has the choice between 4 types of filter: Butterworth, Chebyshev type I, Chebyshev type II, and elliptic. The filter order can be determined using the functions buttord, cheb1ord, cheb2ord and ellipord, respectively. As in the case of the FIR filter, you should achieve a peak passband ripple of 3 dB and minimum stopband attenuation of 30 dB. Which filter type will you choose? Why? Determine the filter order N for the chosen filter type. Design the IIR filter using the function butter, cheby1, cheby2 or ellip, respectively. The frequency response of the filter can be obtained using the function freqz. Plot the designed filter frequency response and see whether the specifications are met. Check especially the passband and stopband edge frequencies. If necessary, redesign the filter.

Apply the filter to the noisy signal using the function filter. For example, if the noisy signal is stored in vector x and the designed IIR filter coefficients are stored in vectors a and b, we can obtain the filtered signal by using y = filter(b, a, x). Plot the Fourier transform of the filtered signal, and compare it with the Fourier transform of the original and of the noisy signal. Discuss your findings.

Summary of basic procedure and useful functions:

1. To read the wave file into a vector, use the wavread function. To play the sound stored in the vector, use the wavplay function.

2. Plot the spectrum using the fft function, refer to the demo ‘filtdem’ on how to plot the spectrum. Wherever possible, use the subplot function to combine the spectra of the noisy and clean signal into a signal plot for easy comparison.

3. Determine the cut-off frequency, transition width, and filter order required. For FIR filter, the filter order needs to be computed manually. For IIR, it can be computed using either the buttord, cheb1ord, cheb2ord and ellipord functions.

4. Design a low-pass filter using. For FIR, use the fir1 function. For IIR, use either the butter, cheby1, cheby2 or ellip functions.

5. Filter the noisy signal using the filter function.

6. Compare the frequency spectrum again to check if the noise has been removed. You can play back the sound too to check if the quality has been improved.

5. Exercise Questions

a) Describe the filter specification used and the results obtained using FIR and IIR filter. Use data tables to tabulate your result.

Write a Matlab code to compute the difference between the frequency samples of the clean signal and the noisy signal. The frequency samples of the signal can be obtained using the

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Matlab fft function. Use the code to estimate the noise frequency range. Your filter design specifications depend on this estimation.

Noise frequency range (Hz): _____

Filter Specification

Sampling frequeny (Hz): ______ Nyquist Frequency (Hz) ____

Passband edge frequency (Hz): ____ Passband frequency range: ______

Stopband edge frequency (Hz): ______ Stopband frequency range: ______

Maximum passband attenuation (dB): _____ Minimum stopband attenuation (dB): ____

i) Tabulate the performance of the FIR filter obtained. Plot the frequency response graph.

FIR Filter Design with Window Method

Implemented filter

Design method used. Mention the window function used.Minimum order attained, N

Attenuation at passband edge frequency. (dB)(Obtained from frequency response graph)Attenuation at stopband edge frequency. (dB)(Obtained from frequency response graph)

Which window function provides the best performance? Justify your answer.

How many filter coefficients is used to implement the FIR filter?

ii) Use Matlab function to estimate the order of the various IIR filter. Write the answer in the table below. Which IIR filter gives the best performance? Explain your answer from the aspect of computational complexity and memory requirement of the filter implementation.

IIR Filter Types Butterworth Type 1 Chebychev

Type 2 Chebychev

Elliptic filter

Minimum order attained, N

Comment on ripples in the passband

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iii) Fill up the table below with the result obtained from the chosen IIR filter. Plot the frequency response graph.

IIR Filter Design with Bilinear Transform

Implemented filter

Design method. Mention the analog prototype filter used.Minimum order attained, N

Attenuation at passband edge frequency. (dB)(Obtained from frequency response graph)Attenuation at stopband edge frequency. (dB)(Obtained from frequency response graph)

b) Based on the filter that you have designed, compare and comment on the performance of the FIR and IIR filter. Explain if the filters designed successfully remove the noise. Explain your answer by referring to the frequency spectrum of the clean, noisy and filtered signal. Explain why the filtered signal is not perceptually similar to the clean signal.

6. Report Writing Guidelines

The lab report shall consist of the following headings. Write clearly and concisely to describe the important elements of your experiment. All the graph and table need to be labeled properly. Describe the data with the proper unit. The answer from the questions in section 5 can be incorporated into the result and discussion section. If there is any numerical calculation, it needs to be shown in the procedure section. Don’t forget to mention the filter specification and design parameters used.

i) A header page which mentions student name and ID number, subject, lab number, date of

experiment and date of reportii) Introduction

This section introduces the basic theory underlying the experiment.iii) Objectives

State the specific investigation that you would like to conduct in this experiment.

iv) Procedures Explain the procedure and the methodology used

v) Results and Discussion Describe the results obtained in a suitable form such as table and graph. Discuss the finding of the experiment from the result obtained.

vi) Conclusion Derive conclusion based on your findings and results

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vii) References

The report must be type written. You are warned that the act of experiment data fabrication, copying of other people work and failure to acknowledge the source of your information in the report (plagiarism) are serious offences and if found, the student will be penalized.

7. Further readings

[1] J. H. McClellan, R. W. Schafer, and M. A. Yoder, Signal Processing First. Upper Saddle River, NJ: Prentice Hall, 2003.

[2] V. K. Ingle and J. G. Proakis, Digital Signal Processing Using MATLAB V.4. Boston: PWS Publishing Company, 1997.

[3] E. C. Ifeachor and B. W. Jervis, Digital Signal Processing: A Practical Approach, 2nd ed. Upper Saddle River, NJ: Prentice Hall, 2001.

[4] M. H. Hayes, Schaum's Outline of Theory and Problems of Digital Signal Processing, New York: McGraw-Hill, 1999.

[5] J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms, and Applications, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1996.

[6] A. V. Oppenheim and R. W. Schafer, Discrete-Time Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.

Marking Scheme

Assessment Components %1 Acquisition of basic MATLAB programming skills. 202 Correctness in calculations and illustrations. 303 Correctness in relating and explaining the results obtained. 304 Depth of discussion. 20

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Demo on the DSP Starter Kit

In DSP2, a demonstration on the use of the DSP starter kit (DSK) for Texas Instrument’s TMS320C55xx processor will be given. In the following, some background information on real-time DSP implementation using general-purpose DSP processors is given.

Overview

The demonstration setup for the real-time DSP implementation consists of Texas Instrument’s DSP Starter Kit, TMS320VC5510 Headphone or Speaker Multimedia PC Code Composer Studio

o A DSP development tool that allows users to create, edit, build, debug and analyse DSP programs either in a simulated environment or actual real-time implementation into the DSP processor.

Matlabo Design of DSP programs such as FIR and IIR filters using common

programming language such a C.

The objective of the setup is to demonstrate the work flow of designing a FIR filter using Matlab (such as FDATool) and to implementation the designed filter onto a real-time DSP processor. During the demonstration you will see that signals are being fed into the DSP board continuously and output will be generated at real-time. This is possible with the DSP processor working multiple times faster that the incoming signal to process and execute according to the loaded program (For this demonstration we have loaded in a simple FIR LPF filter).The work flow begins by designing a FIR filter using Matlab that meets certain requirements such as;

the type of filters; LPF (Low Pass Filter), BPF (Band pass Filter) no. of coefficients

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Block Diagram of TMS320VC5510 DSK Board

Audio signal from PC’s audio out

Headphone


cut-off frequency preferred filter

After the FIR filter has been designed, the filter is simulated under Matlab to obtain information such as its frequency response and stability. This is to ensure that the designed filter meets the stated requirements. Any changes or adjustment may be performed at this stage. After the filter is designed, it will result in a few parameters i.e. the number of filter coefficients, value for each filter coefficient and sampling period. These parameters will be loaded into the DSK board together with a program written in CCS. This program will process the incoming signal with the obtained parameters. For this demonstration, an FIR filter algorithm program is chosen and is written in C language using CCS (Code Composer Studio). The FIR filter algorithm program will utilise the parameters obtained.

The DSK board works under the CCS and communicates with it through its onboard USB (Universal Serial Bus) JTAG Emulator as shown in the figure above. Therefore the FIR filter algorithm program will perform the following task onto the DSK board; (Refer to signal flow graph shown below)

Initiate all the necessary configuration such as sampling period, codec configuration, memory allocation and etc.

Fetch the signal stored in the memory Perform convolution between the stored signal and the coefficient values obtained

earlier using Matlab Stored the resultant value

Signal Flow Graph

The resultant values will then be converted to an output signal via the DAC codec. The output signal will contain the filtered input signal. In other words, the output signal is actually a convolution result between the coefficients and the input signal. During the demonstration, a noisy signal will be the

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Audio signal from PC’s audio output

ADC Codec

Input signal is stored in Memory (SDRAM)

DSP Processor (Convolution of the input signal and the filter coefficients)

Loaded FIR Filter Program via Code Composer Studio

Headphone

DAC Codec

Resultant is stored in Memory (SDRAM)

Filter coefficients


input signal and the DSK board will be programmed to be a FIR filter. The output of the DSK board will be a clean signal with the noise removed.

Throughout the demonstration you will witness the workflow of implementing the theoretical DSP knowledge to a realisable real-time DSP application.

Appendix

Software tools are computer programs that have been written to perform specific operations. Most DSP operations can be categorized as being either analysis tasks or filtering tasks. Signal analysis deals with the measurement of signal properties. MATLAB is a powerful environment for signal analysis and visualization, which are critical components in understanding and developing a DSP system. C programming is an efficient tool for performing signal processing and is portable over different DSP platforms.

MATLAB is an interactive, technical computing environment for scientific and engineering numerical analysis, computation, and visualization. Its strength lies in the fact that complex numerical problems can be solved easily in a fraction of the time required with a programming language such as C. By using its relatively simple programming capability, MATLAB can be easily extended to create new functions, and is further enhanced by numerous toolboxes such as the Signal Processing Toolbox and Filter Design Toolbox. In addition, MATLAB provides many graphical user interface (GUI) tools such as Filter Design and Analysis Tool (FDATool).

The purpose of a programming language is to solve a problem involving the manipulation of information. The purpose of a DSP program is to manipulate signals to solve a specific signal processing problem. High-level languages such as C and C++ are computer languages that have English-like commands and instructions. High-level language programs are usually portable, so they can be recompiled and run on many different computers. Although C/C++is categorized as a high-level language, it can also be written for low-level device drivers. In addition, a C compiler is available for most modern DSP processors such as the TMS320C55x. Thus C programming is the most commonly used high-level language for DSP applications.

C has become the language of choice for many DSP software development engineers not only because it has powerful commands and data structures but also because it can easily be ported on different DSP processors and platforms. The processes of compilation, linking/loading, and execution are outlined in Figure 1.

Figure 1. Program compilation, linking, and execution flow

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C compilers are available for a wide range of computers and DSP processors, thus making the C program the most portable software for DSP applications. Many C programming environments include GUI debugger programs, which are useful in identifying errors in a source program. Debugger programs allow us to see values stored in variables at different points in a program, and to step through the program line by line.

The manufacturers of DSP processors typically provide a set of software tools for the user to develop efficient DSP software. The basic software development tools include C compiler, assembler, linker, and simulator. In order to execute the designed DSP tasks on the target system, the C or assembly programs must be translated into machine code and then linked together to form an executable code. This code conversion process is carried out using software development tools illustrated in Figure 2.

Figure 2 TMS320C55x software development flow and tools

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The TMS320C55x software development tools include a compiler, an assembler, a linker, an archiver, a hex conversion utility, a cross-reference utility, and an absolute lister. The C55x C compiler generates assembly source code from the C source files. The assembler translates assembly source files, either hand-coded by DSP programmers or generated by the C compiler, into machine language object files. The assembly tools use the common object file format (COFF) to facilitate modular programming. Using COFF allows the programmer to define the system’s memory map at link time. This maximizes performance by enabling the programmer to link the code and data objects into specific memory locations. The archiver allows users to collect a group of files into a single archived file. The linker combines object files and libraries into a single executable COFF object module. The hex conversion utility converts a COFF object file into a format that can be downloaded to an EPROM programmer or a flash memory program utility.

The DSK is a low-cost development board for the user to develop and evaluate DSP algorithms under a Windows operation system environment. In this book, we will use the Spectrum Digital’s TMS320VC5510 DSK for real-time experiments. The DSKworks under the Code Composer Studio (CCS) development environment. The DSK package includes a special version of the CCS. The DSK communicates with CCS via its onboard universal serial bus (USB) JTAG emulator. The C5510 DSK uses a 200 MHz TMS320VC5510 DSP processor, an AIC23 stereo CODEC, 8 Mbytes synchronous DRAM, and 512 Kbytes flash memory.

Texas Instruments’ CCS Integrated Development Environment (IDE) is a DSP development tool that allows users to create, edit, build, debug, and analyze DSP programs. For building applications, the CCS provides a project manager to handle the programming project. For debugging purposes, it provides breakpoints, variable watch windows, memory/register/stack viewing windows, probe points to stream data to and from the target, graphical analysis, execution profile, and the capability to display mixed disassembled and C instructions. Another important feature of the CCS is its ability to create and manage large projects from a GUI environment. In this demo, we will use simple examples to show you the basic editing features, key IDE components, and the use of the C55x DSP development tools.

Procedures of the demo are listed as follows:1. Create a project for the CCS: Choose Project→New to create a new

project file and save it. The CCS uses the project to operate its built-in utilities to create a full-build application.

2. Create C program files using the CCS editor: Choose File→New to create a new file, type in the C code and save it as a C source file.

3. Create a linker command file for the simulator: The command file (with extension .cmd) is used by the linker to map different program segments into a prepartitioned system memory space.

4. Setting up the project: Add the C and cmd files to the project by choosing Project→Add Files to Project. Programs written in C language require the use of the run-time support library, either rts55.lib or

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rts55x.lib, for system initialization. This can be done by selecting the compiler and linker dialog box and entering the C55x run-time support library, rts55.lib, and adding the header file path related to the source file directory.

5. Build and run the program: Use Project→Rebuild All command to build the project. If there are no errors, the CCS will generate the executable output file (extension .out). Before we can run the program, we need to load the executable output file to the C55x DSK or the simulator. To do so, use File→Load Program menu and select the .out file and load it. Execute this program by choosing Debug→Run. The processor status at the bottom-left-hand corner of the CCS will change from CPU HALTED to CPU RUNNING. The running process can be stopped by the Debug→Halt command. We can continue the program by reissuing the Run command or exiting the DSK or the simulator by choosing File→Exit menu.

The photo of the C5510 DSK is shown in Figure 3. A CCS screen snapshot is shown in Figure 4.

Figure 3 TMSVC 5510 DSK

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Figure 4 CCS screen snapshot of the example using CCS

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Appendix: Characteristics of window functions

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