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(solution) can you help me write this code? i need help as soon as possible.

can you help me write this code? i need help as soon as possible. this is due friday

Electrical and Computer Engineering Dept.

Dr. Monty A. Escabi

EE 3101, Fall 2015 MATLAB Application Project

Due: Dec. 11, 2015

Digital Audio Crossover Design

The goal of this project is to build and test a digital audio crossover network. A crossover is a filterbank that is

used to separate out sounds so that they are efficiently delivered to a speaker array. Audible sounds cover a

frequency range from 20 Hz ? 20 kHz (for human hearing). Individual speaker drivers (e.g., tweeter, midrange

and woofer), however, can only faithfully reproduce a portion of this spectrum. Therefore, speaker drivers of

different sizes can be combined to reproduce the entire hearing range. The crossover divides the sound spectrum

into the frequencies that each driver faithfully reproduces.

In this project you are to implement a digital crossover in MATLAB for a three-way speaker system. The

schematic for this design is illustrated below: The input sound waveform, x(t) , is decomposed by three filters and these outputs are routed to each individual

speaker drivers. The lowpass (LP) filter encompasses frequencies below 500 Hz. The output from this filter is

used to drive the woofer (low frequency speaker). The midrange driver is driven by the output from a bandpass

(BP) filter. This filter only allows frequencies in the range 500Hz-5 kHz to pass through. The highpass filter

(HP) encompasses the remainder of the audible range (5-22.05 kHz). This output drives the high frequency

speaker (the tweeter).

You are allowed to work in groups of two or three. However, each student is required to hand in their own

independent report and is required to implement his/her own code. Reports using copied code or text will

be returned without a grade! (Yes, we will check for this! Code needs to be uploaded onto HUSKYCT). Group

members who worked on the project should be identified in the writeup. Filter Design

For this project you will use a Kaiser window FIR (Finite Impulse Response) filters to implement the

crossover network. In general, windowed FIR filters are implemented as the product of an ideal filter impulse response ( hideal [ k ] , for a lowpass, bandpass or highpass) and a window function ( w [ k ] ) that is used to

smoothly truncate the ideal filter impulse response to a total of 2N+1 samples

h [ k ] = hideal [ k ] ? w [ k ] Here N is a filter parameter referred to as the filter order where the total number of coefficients in the discrete

time impulse response of the filter is 2N+1. Conceptually, note that any ?ideal? filter requires an infinite amount

of time and therefore an infinite amount of coefficients to implement. As we have discussed in class, practically

this is not possible and the ideal filters cannot be implemented. To overcome this, the window function is used

to truncate the filter in a smooth fashion in order to 1) require only finite number of time samples (2N+1) and 2)

minimize filter distortions (or errors) in the passband and stopband.

There are a variety of window functions that can be used to generate an FIR filter as above. In this

project we will be using a Kaiser window. For the Kaiser window, there is an additional parameter ( ? ) that is

used to control the filter ?smoothness? in the time domain or equivalently, it serves to adjust the passband and

stopband specifications (error and frequencies) in the frequency domain.

In the above equation, each of the ideal filter impulse responses are given by

Lowpass

hideal [ k ] = ! 2? Fc $

2Fc

sinc #

k&

Fs

" Fs % where Fc is the cutoff frequency of the idel lowpass filter and Fs =44100 Hz is the sampling rate of the data.

Bandpass

hideal [ k ] = ! BW $

BW

sinc #

? k & ? cos ( 2? ( Fc1 + BW / 2 ) k Fs )

Fs

" Fs

% where Fc1 and Fc2 are the lower and upper cutoff frequencies of the ideal bandpass filter and BW = Fc2 ? Fc1 is

the filter bandwidth.

Highpass

hideal [ k ] = ? [ k ] ? " 2? Fc %

2Fc

sinc $

k'

Fs

# Fs & where Fc is the ideal highpass filter cutoff frequency and ? [ k ] is the discrete time Diract delta function (i.e., 1

for k=0 and zero otherwise). Note that this highpass filter impulse response is simply the difference between an

allpass filter ( ? [ k ] ) and an ideal lowpass filter (the sinc function term).

In all of the above filters k spans the integers (from - ? to ? ), however, in the actual filter

implementation described below the filters will be truncated from ?N to N (for a total of 2N+1 coefficients). Also, note that in MATLAB the sinc function is defined as sin (? x ) / (? x ) whereas above and in the book it is

defined as sin(x)/x. You will thus need to remove ? from the sinc function during implementation in

MATLAB. Filter Implementation

To generate the Kaiser filter you will

1) first generate the desired ideal filter impulse as described above but truncate the filter for a total of 2N+1

coefficients (i.e., k=-N ? N). In matlab, you can assign the vectors h_low, h_band, h_high for each of

these filters.

2) You need to determine the Kaiser order (N) and smoothness parameter ( ? ). As described in the class for

the Butterworth filter, the filter paramters are obtain with a mapping equation that converts the filter

specifications to filter parameters. For a Kaiser filter, there are two filter specifications of interest.

a) ATT = is the filter attenuation. Simply speaking, this is the amount of allowed error in the

passband and stopband in units of dB. For Kaiser filters, the stopband and passband errors

are symmetric so we don?t need separate errors to describe each and we can use a single

number. For example, if the error in the passband is ? =0.01 (relative to a passband gain of

1), then ATT = ?20 log10 (? ) = ?20 log10 ( 0.01) = 40 dB (in the graph below ATT=As). For the

case of a bandpass filter, two errors may be provided, one for the stopband and one for the

passband. Choose the more stringent of the two for the Kaiser filter design.

b) TW= is the transition width of the filter in units of Hz. Conceptually, TW is the frequency

range over which the filter transitions from the passband to the stopband (or vise versa for a

highpass filter). If f p is the passband frequency in Hz and fs is the stopband frequency, then

TW= f - f . The filter specifications are shown graphically below for a lowpass filter. p

s

OF MAXIMALLY FLAT AND PROLATE SPHEROIDAL-TYPE FIR FILTERS 703 Equation

polynomials on the interval

where, as indicated in Fig. 2.

is obtained by applyIt is

ral for

result may be expressed as (6) ction that is nonzero on the

Equation (6) is valid for

With

ction,

is recognized to

lter function.

onse of the ?lter is obtained

formula (1) and using the

n

(7) is noted that the magnitude

lse response in (7) approach

or as

Impulse

types, such as bandpass and

similar fashion.

hese prototypes continue to Fig. 3. De?nitions of stopband attenuation

, transition width

, and

cutoff frequency

for the (real-valued) ?lter function

associated with

Note that convolution series (2).

the truncated the cutoff frequency is directly in the center frequencies ( fc = ( fs + f p ) between the stopband and passband

2 ) (normalized to units of radians in the above graph). For the are no additional parameters, and therefore no control is case of aover stopband attenuation may be provided,of for the stopband and one for the

bandpass filter, two TW or over the shape one

provided

passband. Choose the more stringent of the two for the Kaiser filter design.

the magnitude response.

In 1974, Kaiser [3] introduced a window function that

approximated the prolate spheroidal wave function [18] while

being computationally easier to determine. Using the Kaiser

window, stopband attenuation of the magnitude response may

to a parameter of the window,

be controlled by mapping Given the above filter specification (ATT and TW or alternately the stopband and passband frequencies

and errors), you will obtain the filter parameters N and ? using the following transformation #

0

%

%

0.4

? = $ 0.5842 ( ATT ? 21) + 0.07886 ( ATT ? 21)

%

0.1102 ( ATT ? 8.7)

%

& ATT <21

21 ? ATT ? 50

ATT > 50 # F ( ATT ? 7.95) &

N = ceil% s

(

$ 28.72 TW ' Once you obtain the filter parameters, the Kaiser window is obtain as a vector containing 2N+1 coefficients

using the following command in MATLAB:

w=kaiser(2*N+1,Beta);

If you generate the ideal filter impulse response as described above, the final filter is then generated as

h=h_ideal.*w;

where h_ideal is a vector containing the filter coefficients from the above equations.

Crossover Network Specifications

The filters will be designed to satisfy the following specifications.

Lowpass Filter:

G p = 0.9

f p = 250 Hz Gs = 0.01

fs = 750 Hz Bandpass Filter:

G p1 = G p2 = 0.9

f p1 = 700 Hz

f p2 = 4.5 kHz Gs1 = Gs2 = 0.01

fs1 = 300 Hz

fs2 = 5.5 kHz Highpass Filter:

G p = 0.9 f p = 5.5 kHz Gs = 0.01

fs = 4.5 kHz All sound waveform are sampled at a sampling rate of 44.1 kHz. Once you generate the Kaiser impulse

response as described above you can simply convolve the impulse response with the sound waveform to

generate the output:

y=conv(h,x);

where h is the impulse response vector, x is the input sound vector, and y is the output sound vector. Simulation

You are to simulate the crossover filtering procedure for two separate signals.

1) White noise ? similar to the hissing noise you hear when your FM radio is not tuned to a station

2) Music sound sample provided

I will provide a short music sound sample as a MATLAB data file (to load type: ?load filename.mat?). To

generate a sample of white noise simply use the command:

x=randn(1,44100*5);

This generates a 5 second white noise signal.

Note that once your code is written for white noise you simply have to change the array x to resimulate the

crossover filterbank for music. No additional work is necessary! Plotting Results

You are required to plot the results both in the time and frequency domain for both the input and the output. To

plot the time-waveforms simply use the command:

plot(taxis,x)

where taxis is a time axis array containing the sample time points and x is an array containing the waveform. To

plot the magnitude spectrum of the input or output signals simply use the power spectral density command

(PSD; type ?help psd? for details). The syntax will typically be something as follows:

psd(x,512,Fs)

where Fs=44100 is the sampling rate.

Note that there are a total of 12 graphs to plot (2 sounds x 3 filters/sound * (1 time domain graph/filter + 1 freq.

domain graph/filter) = 12 graphs) Generating WAV Sound File and playing the sound:

You are expected to listen to each of the sound files at each filter output stage of the crossover so that you gain

some insight into the transformations that are being performed at each stage. In order to do this, you need to

convert each data array into a WAV sound file. You can do this with the WAVWRITE command in MATLAB.

The syntax for the command is as follows:

wavwrite(Y,Fs,nbits,wavfile)

where

Y= is the signal vector

Fs= is the sampling frequency in Hz

nbits=is the number of bits (i.e. The resolution) of each sample

wavfile=is the output file name For additional information in MATLAB you can type: help wavwrite

For this project, you will use the parameters

Fs=44100

nbits=16

Lets say you want to write the data of an array X to a WAV file named: test.wav. You can use the syntax:

wavwrite(X,44100,16,?test.wav?);

Alternately, on some computers, you can also listen to the sound using the SOUNDSC command. For the sound

vector X, you can play the sound using the syntax: soundsc(X,Fs). You can use this as an alternative approach

(to WAVWRITE) assuming that it works on your computer. Writeup

A short writeup describing the methods and results is required. At minimal it should include.

1) A brief introduction describing the project objective (~ 1 page).

2) A brief description of the methods (~1-2 pages).

3) A discussion of the results. Focus on describing:

a) How each filter modifies the time waveforms for music and white noise.

b) How each filter modifies the magnitude spectrum of music and white noise.

c) Compare and contrast the input and output waveforms.

d) Listen to the input and output sounds using the wavwri routine and compare and contrast the

audible differences between the input and output.

4) Program code.

5) Figures. Honors Student Requirements

In addition to implementing the filterbank using the Kaiser filters, also implement the above filterbank

(identical passband and stopband parameters) using a Butterworth filter. Details for the implementation of the

Butterworth filters in MATLAB can be found in section 7.5 of the book (e.g., see example C7.5). As for the

Kaiser filter, plot the same set of results (time and frequency domain), but only do so for white noise.

a) Contrast and compare the filters (Kaiser vs. Butterworht). Do they sound similar or are there audible

differences when you implement the crossover for the music. Visually, do the spectrums look similar or

different when you use white noise. Describe any discrepancies or similarities.

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