AI in Bird Sound Identification Quiz

Answers and explanations appear as you take the quiz.

1. What intermediate representation do most bird-sound AI systems analyze?
   • A spectrogram showing frequency over time
   • A raw text transcript of the call
   • A GPS coordinate of the bird
   • A photograph of the bird's plumage
2. Which AI model powers the Merlin Sound ID feature?
   • WhaleNet
   • BirdNET
   • SongScope
   • AviaGPT
3. Why is acoustic monitoring especially useful for birds?
   • Birds are silent most of the year
   • Birds only call underwater
   • Birds are often easier to hear than to see
   • Cameras cannot photograph birds
4. What is a common challenge for bird-sound classifiers?
   • Birds never repeat their calls
   • Spectrograms cannot be computed
   • All birds sound identical
   • Overlapping calls and background noise reduce accuracy
5. Which resource provides labeled recordings used to train these models?
   • Xeno-canto
   • The phone book
   • Google Maps
   • A tide chart
6. What advantage do low-power 'edge' devices offer for future bird monitoring?
   • They eliminate all bird calls
   • They identify species on-site and transmit only detections
   • They require constant human supervision
   • They only work indoors
7. Which ecological patterns can long-running autonomous bird recorders help map?
   • Only the color of a bird's feathers in sunlight
   • The exact number of eggs in every nest
   • Species presence, abundance, and migration timing
   • Changes in soil chemistry beneath each microphone
8. What can nocturnal flight-call analysis help researchers monitor?
   • Fish feeding activity beneath coral reefs
   • Bee colony weight changes during winter
   • The color temperature of city street lighting
   • Migrating birds vocalizing as they pass overhead at night