Audio compression basics in Signal Processing - Mini Project: Build & Apply

Audio compression basics

📖 Scenario: You have recorded a short audio clip and want to reduce its size for easier sharing. Audio compression helps by simplifying the sound data while keeping it clear enough to understand.

🎯 Goal: Learn how to create a simple audio data list, set a compression threshold, filter out quiet sounds below that threshold, and see the compressed audio data.

📋 What You'll Learn

Create a list of audio sample amplitudes

Set a compression threshold value

Use a list comprehension to keep only samples above the threshold

Print the compressed audio data list

💡 Why This Matters

🌍 Real World

Audio compression is used in music streaming, phone calls, and podcasts to reduce file size and save bandwidth while keeping sound quality good.

💼 Career

Understanding basic audio compression helps in roles like audio engineering, software development for media apps, and data science involving sound data.

1

Create audio sample data

Create a list called audio_samples with these exact amplitude values: 0.1, 0.5, 0.05, 0.7, 0.2, 0.9, 0.03, 0.4.

Signal Processing

# Create the list audio_samples with the given values
# Your code here

Hint

Use square brackets to create a list and separate values with commas.

2

Set compression threshold

Create a variable called threshold and set it to 0.2 to filter out quiet sounds below this value.

Signal Processing

audio_samples = [0.1, 0.5, 0.05, 0.7, 0.2, 0.9, 0.03, 0.4]
# Set the threshold variable to 0.2
# Your code here

Hint

Use a simple assignment to create the threshold variable.

3

Filter audio samples above threshold

Use a list comprehension to create a new list called compressed_audio that contains only the values from audio_samples that are greater than or equal to threshold.

Signal Processing

audio_samples = [0.1, 0.5, 0.05, 0.7, 0.2, 0.9, 0.03, 0.4]
threshold = 0.2
# Create compressed_audio with samples >= threshold using list comprehension
# Your code here

Hint

Use [sample for sample in audio_samples if sample >= threshold] to filter the list.

4

Display compressed audio data

Print the compressed_audio list to see the filtered audio samples after compression.

Signal Processing

audio_samples = [0.1, 0.5, 0.05, 0.7, 0.2, 0.9, 0.03, 0.4]
threshold = 0.2
compressed_audio = [sample for sample in audio_samples if sample >= threshold]
# Print the compressed_audio list
# Your code here

Hint

Use print(compressed_audio) to show the result.

Practice

1. What is the main goal of audio compression?

easy

A. To reduce the file size while keeping sound quality acceptable

B. To increase the file size for better quality

C. To convert audio files into video files

D. To remove all background noise completely

Solution

1. Step 1: Understand the purpose of audio compression

   Audio compression aims to make audio files smaller so they use less space and bandwidth.

2. Step 2: Identify the correct goal

   The goal is to reduce size but keep the sound quality good enough for listening.

3. Final Answer:

   To reduce the file size while keeping sound quality acceptable -> Option A

4. Quick Check:

   Audio compression = smaller files with good quality [OK]

Hint: Compression means smaller size, not bigger [OK]

Common Mistakes:

• Thinking compression increases file size
• Confusing audio with video conversion
• Assuming all noise is removed

2. Which of the following is a correct statement about lossy audio compression?

easy

A. It keeps all original audio data intact

B. It removes some audio data permanently to reduce size

C. It converts audio to text format

D. It increases the audio file size

Solution

1. Step 1: Define lossy compression

   Lossy compression removes some audio data permanently to save space.

2. Step 2: Compare options

   Only It removes some audio data permanently to reduce size correctly describes lossy compression; others are incorrect or unrelated.

3. Final Answer:

   It removes some audio data permanently to reduce size -> Option B

4. Quick Check:

   Lossy = data removed permanently [OK]

Hint: Lossy means some data is lost forever [OK]

Common Mistakes:

• Confusing lossy with lossless compression
• Thinking lossy keeps all data
• Believing audio is converted to text

3. Consider this Python code snippet simulating audio compression bitrate calculation:

original_bitrate = 1411  # in kbps for CD quality
compression_ratio = 0.1
compressed_bitrate = original_bitrate * compression_ratio
print(int(compressed_bitrate))

What is the output of this code?

medium

A. 1411

B. 14

C. 141

D. 0

Solution

1. Step 1: Calculate compressed bitrate

   Multiply original bitrate 1411 kbps by compression ratio 0.1: 1411 * 0.1 = 141.1

2. Step 2: Convert to integer and print

   int(141.1) is 141, so the output is 141.

3. Final Answer:

   141 -> Option C

4. Quick Check:

   1411 * 0.1 = 141 [OK]

Hint: Multiply bitrate by ratio, then convert to int [OK]

Common Mistakes:

• Forgetting to multiply by compression ratio
• Confusing int conversion rounding
• Printing original bitrate instead

4. This code tries to calculate compressed audio size but has an error:

file_size_mb = 50
compression_rate = 0.2
compressed_size = file_size_mb / compression_rate
print(compressed_size)

What is the error in this code?

medium

A. Division by zero error

B. Variable names are invalid

C. Missing import statement

D. It should multiply, not divide, by compression_rate

Solution

1. Step 1: Understand compression calculation

   Compressed size should be original size multiplied by compression rate (fraction less than 1).

2. Step 2: Identify the error in code

   The code divides file_size_mb by compression_rate, which increases size incorrectly.

3. Final Answer:

   It should multiply, not divide, by compression_rate -> Option D

4. Quick Check:

   Compression size = original size * rate [OK]

Hint: Compression reduces size, so multiply by rate [OK]

Common Mistakes:

• Using division instead of multiplication
• Assuming variable names cause error
• Expecting import needed for math

5. You have a 100 MB audio file and want to compress it to about 25% of its size using lossy compression. Which bitrate should you choose if the original bitrate is 160 kbps?

Options:
A) 40 kbps
B) 80 kbps
C) 120 kbps
D) 160 kbps

hard

A. 40 kbps

B. 80 kbps

C. 120 kbps

D. 160 kbps

Solution

1. Step 1: Calculate target bitrate for 25% size

   25% means 0.25 times original bitrate: 160 kbps * 0.25 = 40 kbps.

2. Step 2: Choose bitrate matching target

   40 kbps is 40 kbps, which matches the target bitrate for 25% size.

3. Final Answer:

   40 kbps -> Option A

4. Quick Check:

   160 * 0.25 = 40 kbps [OK]

Hint: Multiply original bitrate by compression fraction [OK]

Common Mistakes:

• Choosing bitrate higher than target size
• Confusing percentage with absolute value
• Ignoring lossy compression effect