Music Auto-Tagging Quiz

Answers and explanations appear as you take the quiz.

1. Why do multi-label music taggers often use separate sigmoid outputs rather than a single softmax over tags?
   • All tag probabilities must sum to one
   • A recording is required to have exactly one tag
   • Sigmoid outputs eliminate the need for labeled examples
   • Several nonexclusive tags can apply to the same recording
2. What input representation do most modern auto-tagging models feed into their neural network?
   • A mel-spectrogram (a time-frequency image)
   • The raw waveform amplitude values only
   • The MP3 file's metadata fields
   • Sheet-music notation
3. Why is the mel scale used instead of a plain linear frequency scale?
   • It compresses files more efficiently
   • It removes all background noise
   • It approximates how humans perceive pitch
   • It is required by copyright law
4. What is a major challenge when training auto-tagging models on real-world datasets?
   • Audio files are too small to learn from
   • Human-applied tags are noisy, incomplete, and imbalanced
   • There are no public music datasets
   • Spectrograms cannot be computed in real time
5. Which dataset is commonly used to train and benchmark music auto-tagging systems?
   • MagnaTagATune
   • ImageNet
   • SQuAD
   • LibriSpeech
6. What additional search interface can an audio-language model such as CLAP support for a music catalog?
   • Guaranteed identification of every artist without audio input
   • Automatic replacement of all rights metadata
   • Searching only by the recording's compressed file size
   • Retrieval using free-form text descriptions rather than only fixed tag names
7. Which objective commonly trains separate binary tag predictions in a music auto-tagger?
   • A single mutually exclusive genre softmax only
   • Binary cross-entropy over the tag outputs
   • A pixel-reconstruction loss on album artwork only
   • A loss requiring exactly one positive tag per track
8. If a music dataset's annotations are incomplete, how should an absent instrument tag be interpreted?
   • It proves the recording has no instruments
   • It guarantees that the tagger's positive prediction is wrong
   • It may be unannotated rather than reliable evidence that the instrument is absent
   • It means the waveform cannot be used for evaluation