Music Genre Classification Quiz

Answers and explanations appear as you take the quiz.

1. What is a mel-spectrogram, commonly used as input to genre-classifying neural networks?
   • A time-frequency image of audio using a perceptual pitch scale
   • A list of song lyrics converted to numbers
   • A waveform showing raw amplitude over time only
   • A database of artist names and release years
2. Which classic benchmark dataset contains 1,000 thirty-second clips across 10 genres?
   • ImageNet
   • GTZAN
   • LibriSpeech
   • COCO
3. Why is achieving perfect genre-classification accuracy fundamentally difficult?
   • Computers cannot process audio at all
   • All songs sound identical to algorithms
   • Genre boundaries are fuzzy and culturally subjective
   • Audio files are too small to analyze
4. Which hand-crafted feature was widely used in early genre classification systems?
   • JPEG compression ratio
   • HTML tag frequency
   • Pixel brightness histograms
   • Mel-frequency cepstral coefficients (MFCCs)
5. What does a softmax layer at the end of a genre classifier produce?
   • A probability distribution over genre classes
   • A single audio waveform
   • A compressed MP3 file
   • A spectrogram image
6. How can self-supervised music pretraining reduce dependence on labeled genre examples?
   • It eliminates the need to evaluate genre labels
   • It can learn audio representations from unlabeled music, which are then evaluated or fine-tuned with labeled examples
   • It guarantees correct labels for every song
   • It converts genre categories into universal facts
7. How can exact or repeated excerpts in GTZAN distort a random train/test evaluation?
   • They ensure that every genre label is objective
   • They make the test set contain more genres than training
   • If repetitions cross the split, a model may see duplicate or near-duplicate material in both sets
   • They remove any need to inspect class labels
8. How do MFCCs differ from a mel-spectrogram representation?
   • MFCCs are the model’s final genre probabilities
   • A mel-spectrogram removes the time axis entirely
   • MFCCs measure only the recording’s file size
   • MFCCs summarize the log-mel spectrum with cepstral coefficients