Audio Chord Recognition Quiz

Answers and explanations appear as you take the quiz.

1. What is the output of an audio chord recognition system?
   • A time-aligned sequence of chord labels
   • A list of song lyrics
   • The recording's file size
   • A photo of the band
2. Which feature is most central to chord recognition?
   • File modification date
   • Chroma (pitch-class) vectors
   • Stereo channel count
   • Bitrate
3. Why is harmonic-percussive separation often applied before chord recognition?
   • To make the song louder
   • To translate the lyrics
   • To suppress drum noise that obscures harmony
   • To shorten the song
4. What role did Hidden Markov Models traditionally play in chord recognition?
   • Generating album artwork
   • Compressing the audio
   • Tuning the guitar
   • Smoothing chord sequences using likely transitions
5. What can small tuning differences do to chroma-based chord recognition?
   • Shift energy between nearby pitch bins and confuse closely related chord labels
   • Remove all temporal information from the recording
   • Make every chord label equally likely
   • Convert chroma features into text tokens
6. Which app is a well-known example of chord recognition for learners?
   • A spreadsheet program
   • Chordify
   • A weather app
   • A flight tracker
7. Which two processes does Chordify identify in placing chord labels on a song?
   • Speaker diarization and voice activity detection
   • Source separation and image captioning
   • Chord recognition and beat tracking
   • Pitch correction and text-to-speech
8. How did Lee and Slaney create supervised training labels for their HMM chord-recognition method?
   • They used only unlabelled live recordings
   • They transcribed lyrics and inferred chords from word sentiment
   • They used random labels and optimized without audio
   • They generated labels by harmonic analysis of symbolic music and paired them with audio rendered from the same symbolic data