Audio signal characteristics in Signal Processing - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the duration of an audio signal in seconds.

Signal Processing

duration = len(audio_signal) / [1]

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

Abit_depth

Bsample_rate

Cchannels

Dfrequency

Attempts:

3 left

💡 Hint

Common Mistakes

Using bit depth or channels instead of sample rate to calculate duration.

The duration of an audio signal is the number of samples divided by the sample rate.

2fill in blank

medium

Complete the code to compute the root mean square (RMS) value of an audio signal.

Signal Processing

rms_value = (sum(x**2 for x in audio_signal) / [1]) ** 0.5

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

Alen(audio_signal)

Bchannels

Csample_rate

Dbit_depth

Attempts:

3 left

💡 Hint

Common Mistakes

Dividing by sample rate or channels instead of number of samples.

RMS is calculated by taking the square root of the average of squared sample values. The average is over the number of samples.

3fill in blank

hard

Fix the error in the code to calculate the peak amplitude of an audio signal.

Signal Processing

peak_amplitude = max([1] for x in audio_signal)

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

Ax

Bx**2

Cabs(x)

D-x

Attempts:

3 left

💡 Hint

Common Mistakes

Using raw sample values without abs(), missing negative peaks.

The peak amplitude is the maximum absolute value of the samples, to account for negative values.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Signal Processing

lengths = {word: [1] for word in words if len(word) [2] 3}

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for values.

Using '<' instead of '>' in condition.

This dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary of uppercase keys and their values greater than zero.

Signal Processing

result = [1]: [2] for k, v in data.items() if v [3] 0

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

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using '<' instead of '>' in condition.

This dictionary comprehension creates keys as uppercase versions of original keys and includes only items with values greater than zero.

Practice

1. Which characteristic of an audio signal determines how many samples are taken per second?

easy

A. Duration

B. Amplitude

C. Frequency

D. Sampling rate

Solution

1. Step 1: Understand sampling rate meaning

   Sampling rate is the number of samples recorded per second in an audio signal.

2. Step 2: Compare with other characteristics

   Amplitude is signal strength, frequency is pitch, duration is length of sound.

3. Final Answer:

   Sampling rate -> Option D

4. Quick Check:

   Samples per second = Sampling rate [OK]

Hint: Sampling rate = samples per second in audio [OK]

Common Mistakes:

• Confusing frequency with sampling rate
• Thinking amplitude controls sample count
• Mixing duration with sampling rate

2. Which of the following is the correct way to represent a sine wave signal in Python using numpy for frequency 440 Hz and sampling rate 8000 Hz?

easy

A. t = np.linspace(0, 1, 8000); signal = np.sin(2 * np.pi * 440 * t)

B. t = np.arange(0, 1, 440); signal = np.sin(2 * np.pi * 8000 * t)

C. t = np.linspace(0, 440, 8000); signal = np.sin(2 * np.pi * t)

D. t = np.arange(0, 1, 1/440); signal = np.sin(2 * np.pi * 8000 * t)

Solution

1. Step 1: Define time vector correctly

   Use np.linspace(0, 1, 8000) to create 1 second with 8000 samples.

2. Step 2: Calculate sine wave with correct frequency

   Signal = sin(2π * frequency * time), so 2 * np.pi * 440 * t is correct.

3. Final Answer:

   t = np.linspace(0, 1, 8000); signal = np.sin(2 * np.pi * 440 * t) -> Option A

4. Quick Check:

   Time vector and frequency match signal formula [OK]

Hint: Time vector length = sampling rate, sine uses 2πf t [OK]

Common Mistakes:

• Using np.arange with wrong step size
• Swapping frequency and sampling rate
• Incorrect time vector range

3. Given this Python code snippet, what is the maximum amplitude value of the generated signal?

import numpy as np
sampling_rate = 1000
frequency = 5
 t = np.linspace(0, 1, sampling_rate)
signal = 0.5 * np.sin(2 * np.pi * frequency * t)

medium

A. 1.0

B. 0.5

C. 5.0

D. 0.05

Solution

1. Step 1: Understand amplitude scaling

   The sine function outputs values between -1 and 1. Multiplying by 0.5 scales amplitude to ±0.5.

2. Step 2: Identify maximum amplitude

   Maximum amplitude is the positive peak, which is 0.5 here.

3. Final Answer:

   0.5 -> Option B

4. Quick Check:

   Amplitude scale factor = 0.5 [OK]

Hint: Amplitude scales sine peak values directly [OK]

Common Mistakes:

• Confusing frequency with amplitude
• Ignoring the 0.5 multiplier
• Assuming sine max is always 1 without scaling

4. Identify the error in this code that generates a 1-second 440 Hz sine wave sampled at 8000 Hz:

import numpy as np
t = np.linspace(0, 1, 440)
signal = np.sin(2 * np.pi * 440 * t)

medium

A. The sine function should use cosine instead

B. The frequency should be 8000 instead of 440

C. The time vector length should be 8000, not 440

D. The sampling rate should be 440 instead of 8000

Solution

1. Step 1: Check time vector length for sampling rate

   Sampling rate is 8000 Hz, so time vector must have 8000 points for 1 second.

2. Step 2: Identify mismatch in code

   Code uses 440 points in time vector, which is incorrect for 8000 Hz sampling.

3. Final Answer:

   The time vector length should be 8000, not 440 -> Option C

4. Quick Check:

   Sampling rate = time vector length [OK]

Hint: Time vector length = sampling rate x duration [OK]

Common Mistakes:

• Confusing frequency with sampling rate
• Changing sine to cosine unnecessarily
• Using wrong sampling rate value

5. You have a recorded audio signal sampled at 16000 Hz with a duration of 2 seconds. You want to extract a 440 Hz sine wave component from it. Which steps correctly describe this process?

hard

A. Apply a bandpass filter around 440 Hz, then analyze amplitude and phase

B. Downsample the signal to 440 Hz, then compute FFT

C. Increase amplitude to 16000, then apply lowpass filter at 2 Hz

D. Use a highpass filter at 2 seconds, then normalize frequency

Solution

1. Step 1: Use bandpass filter around target frequency

   To isolate 440 Hz, apply a bandpass filter centered near 440 Hz to keep that frequency and remove others.

2. Step 2: Analyze filtered signal

   After filtering, analyze amplitude and phase to understand the sine wave component.

3. Final Answer:

   Apply a bandpass filter around 440 Hz, then analyze amplitude and phase -> Option A

4. Quick Check:

   Bandpass filter isolates frequency component [OK]

Hint: Bandpass filter isolates specific frequency [OK]

Common Mistakes:

• Downsampling to frequency value instead of sampling rate
• Confusing filter types and cutoff frequencies
• Misunderstanding duration as filter parameter