Symbolic Music Generation Quiz

Answers and explanations appear as you take the quiz.

1. What does symbolic music generation actually produce?
   • Structured notation like notes, pitches, and timing (often MIDI)
   • Finished stereo audio with realistic vocals
   • Only lyrics, no music
   • Album cover artwork
2. What scale did OpenAI report for MuseNet’s generated compositions?
   • Single notes on one instrument only
   • Compositions up to four minutes with ten different instruments
   • One-second clips with no instruments
   • Full orchestral scores limited to 100 measures
3. Which of these is a well-known symbolic music model from OpenAI?
   • Stable Diffusion
   • Whisper
   • MuseNet
   • DALL-E
4. How do modern symbolic models typically treat music computationally?
   • As images of waveforms
   • As random noise with no structure
   • As a single number per song
   • As a language, where note events are tokens predicted in sequence
5. Why can symbolic note-event sequences be shorter than raw-audio waveforms for a musical passage?
   • They represent musical events directly instead of every waveform sample
   • They remove all timing information
   • They store only a single note per song
   • They encode sound as images
6. What is needed to actually hear a symbolic composition?
   • Nothing — it plays itself as finished audio
   • A synthesizer or sampler to render the notes into sound
   • A printing press
   • A separate language model to write lyrics
7. Which note-event properties can a symbolic sequence encode?
   • Only the waveform’s file size
   • Camera exposure and frame rate
   • Pitch, onset, duration, and velocity
   • The listener’s heart rate only
8. Which attributes did MuseNet combine in its note tokens?
   • Only the note’s printed letter name
   • Lyrics, album art, and release date
   • Waveform samples and speaker identity
   • Pitch, volume, and instrument information