Window functions in MATLAB - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a Hamming window of length 50.

MATLAB

w = [1](50);

Drag options to blanks, or click blank then click option'

Ahann

Bblackman

Chamming

Drectwin

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'hann' instead of 'hamming' changes the window type.

Using 'rectwin' creates a rectangular window, not Hamming.

The hamming function creates a Hamming window in MATLAB.

2fill in blank

medium

Complete the code to apply a Blackman window to signal x.

MATLAB

w = [1](length(x));
y_windowed = x .* w;

Drag options to blanks, or click blank then click option'

Akaiser

Bhamming

Cbartlett

Dblackman

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'hamming' instead of 'blackman' changes the window shape.

Using 'kaiser' requires an additional parameter.

The blackman function generates a Blackman window matching the signal length.

3fill in blank

hard

Fix the error in the code to generate a Kaiser window with beta=5 and length 100.

MATLAB

beta = 5;
w = [1](100, beta);

Drag options to blanks, or click blank then click option'

Akaiser

Bhamming

Cblackman

Dhann

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'hamming' or 'hann' which do not accept beta parameter.

Using 'blackman' which does not take beta.

The kaiser function requires the length and beta parameter to generate the window.

4fill in blank

hard

Fill both blanks to create a Bartlett window and normalize it.

MATLAB

w = [1](N);
w = w / [2](w);

Drag options to blanks, or click blank then click option'

Abartlett

Bmax

Csum

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum or mean for normalization changes the scale incorrectly.

Using wrong window function changes the window shape.

The bartlett function creates the window, and dividing by max(w) normalizes it to 1.

5fill in blank

hard

Fill all three blanks to create a Hann window, apply it to signal y, and compute the windowed signal's energy.

MATLAB

w = [1](length(y));
y_win = y .* [2];
energy = sum(y_win [3] 2);

Drag options to blanks, or click blank then click option'

Ahann

Bw

C.^

D.*

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of element-wise '.*' for applying window.

Using '^' instead of element-wise '.^' for squaring.

The hann function creates the window, which is applied element-wise to y using w. The energy is the sum of squares, computed with .^ 2.

Practice

1. What is the primary purpose of using a window function in MATLAB signal processing?

easy

A. To reduce edge effects and smooth the signal

B. To increase the signal amplitude

C. To convert the signal from time to frequency domain

D. To generate random noise

Solution

1. Step 1: Understand window function role

   Window functions are used to taper the edges of a signal to reduce artifacts when processing.

2. Step 2: Identify the effect on signal

   They smooth the signal edges and reduce spectral leakage, not amplify or transform the signal.

3. Final Answer:

   To reduce edge effects and smooth the signal -> Option A

4. Quick Check:

   Window functions reduce edge effects [OK]

Hint: Window functions smooth edges to reduce artifacts [OK]

Common Mistakes:

• Thinking window functions amplify signals
• Confusing windowing with Fourier transform
• Assuming window functions add noise

2. Which of the following is the correct MATLAB syntax to create a Hamming window of length 64?

easy

A. hamming(64)

B. hamming[64]

C. window(hamming, 64)

D. window('hamming', 64)

Solution

1. Step 1: Recall MATLAB window function syntax

   MATLAB provides direct functions like hamming(N) to create windows of length N.

2. Step 2: Check each option

   hamming(64) uses correct function call syntax: hamming(64). window('hamming', 64) uses string incorrectly, C misses quotes, B uses wrong brackets.

3. Final Answer:

   hamming(64) -> Option A

4. Quick Check:

   Use hamming(N) to create Hamming window [OK]

Hint: Use hamming(N) directly, not window('hamming', N) [OK]

Common Mistakes:

• Using quotes incorrectly in function calls
• Using square brackets instead of parentheses
• Confusing window function with generic window()

3. What is the output size of the following MATLAB code?

win = blackman(100);
size_win = size(win);

medium

A. [1 100]

B. [100 100]

C. [100 1]

D. [1 1]

Solution

1. Step 1: Understand blackman output shape

   blackman(N) returns a column vector of length N, so size is N rows by 1 column.

2. Step 2: Apply to N=100

   Output is 100 rows and 1 column, so size is [100 1].

3. Final Answer:

   [100 1] -> Option C

4. Quick Check:

   blackman(N) returns column vector size [N 1] [OK]

Hint: Window functions return column vectors by default [OK]

Common Mistakes:

• Assuming output is a row vector
• Confusing size with length
• Expecting square matrix output

4. Identify the error in this MATLAB code snippet:

win = window('hann', 50);
plot(win);

medium

A. The 'hann' window is not supported in MATLAB

B. The plot function cannot plot window vectors

C. The window length must be greater than 100

D. The window function does not accept string arguments

Solution

1. Step 1: Check window function usage

   In MATLAB, window functions like hann are called directly, not via window('hann', N).

2. Step 2: Validate other options

   Plot can plot vectors, length 50 is valid, and 'hann' window is supported directly.

3. Final Answer:

   The window function does not accept string arguments -> Option D

4. Quick Check:

   Use hann(N), not window('hann', N) [OK]

Hint: Call hann(N) directly, not window('hann', N) [OK]

Common Mistakes:

• Using window() with string instead of direct function
• Thinking plot can't display vectors
• Assuming window length limits

5. You want to apply a Hamming window to a noisy signal vector sig of length 256 to reduce spectral leakage before FFT. Which MATLAB code correctly applies the window and computes the FFT?

hard

A. win = hamming(length(sig)); fft_sig = fft(sig + win);

B. win = hamming(256); fft_sig = fft(sig .* win);

C. win = hamming(256); fft_sig = fft(sig / win);

D. win = hamming(256); fft_sig = fft(sig - win);

Solution

1. Step 1: Create window matching signal length

   Use hamming(256) or hamming(length(sig)) to create the window vector.

2. Step 2: Apply window by element-wise multiplication

   Multiply signal by window element-wise to taper edges before FFT.

3. Step 3: Compute FFT of windowed signal

   Use fft(sig .* win) to get frequency spectrum of windowed signal.

4. Final Answer:

   win = hamming(256); fft_sig = fft(sig .* win); -> Option B

5. Quick Check:

   Window applied by multiplication before FFT [OK]

Hint: Multiply signal by window before FFT [OK]

Common Mistakes:

• Adding or subtracting window instead of multiplying
• Dividing signal by window
• Using window length different from signal length