Audio Data Augmentation Quiz

Answers and explanations appear as you take the quiz.

1. Why apply label-preserving audio augmentation?
   • Make every recording longer regardless of its label
   • Alter labels to balance the dataset
   • Expose the model to plausible variations that keep the target label valid
   • Apply transformations to validation clips during tuning
2. What does convolution with a room impulse response approximate?
   • Mixing a background noise recording
   • Room acoustics and reverberation
   • Changing playback speed
   • Simulating compression artifacts
3. What usually happens to pitch during basic speed perturbation?
   • It remains exactly fixed during ordinary playback-rate changes
   • It changes only if the transcript labels are updated
   • It is automatically restored by resampling at any rate
   • It changes along with playback rate
4. Why can mixing two labeled sound clips require target changes?
   • Both sound events may be present in the mixture
   • Mixing guarantees that only the louder source's label remains valid
   • Every mixture retains exactly one original class by definition
   • The combined waveform automatically removes the second event
5. Which data should receive random training augmentation?
   • Training and validation clips, including random transforms during scoring
   • Training plus validation examples so model selection sees augmented copies
   • Training data only
   • Apply the same random transform to train and test
6. Why split original recordings before generating augmented copies?
   • To avoid using the same random transform settings across folds
   • To prevent transformed versions of one source leaking across partitions
   • To ensure every fold has identical class proportions
   • To preserve all recording conditions in every split
7. Which answer names all three controls for mixing noise without unintended artifacts?
   • Match noise level to a plausible operating range
   • Check for clipping after the signals are added
   • Align waveforms before addition
   • Alignment, power scaling and clipping
8. What risk comes from overly aggressive audio transformations?
   • They can change the label or remove task-relevant signal
   • Any transformation remains label-preserving if the output duration is unchanged
   • Greater distortion always increases deployment accuracy
   • More augmented copies can replace checking whether the target remains audible