Griffin-Lim Spectrogram Inversion Quiz

Answers and explanations appear as you take the quiz.

1. Which missing component does Griffin-Lim estimate when reconstructing a waveform from spectral magnitudes?
   • The original words represented by the signal
   • The recording's speaker identity
   • A phase pattern compatible with a waveform
   • The class labels used by a downstream audio model
2. After inverse STFT, what does a Griffin-Lim iteration do with the new STFT?
   • Sets every phase value to zero before inverse transformation
   • Replaces its magnitudes with the target while retaining estimated phase
   • Updates target magnitudes from the current waveform while keeping the initial phase fixed
   • Stops after inverse STFT without checking a new forward STFT
3. Why is reconstruction from magnitude generally approximate?
   • Magnitude contains the full complex STFT
   • Every spectrogram maps to one exact waveform
   • The algorithm uses no Fourier transform
   • The original phase information has been discarded
4. Which settings should match between analysis and reconstruction?
   • FFT size, window, hop and centering conventions
   • Only the total number of output samples
   • Only the sample rate, regardless of window or hop
   • Any transform settings that produce the same array dimensions
5. Does increasing iteration count guarantee perceptually better audio?
   • Yes, always
   • Yes, if random phase is used
   • No; effects depend on representation and settings
   • No; iterations never change output
6. Which is a common role of Griffin-Lim in a speech project?
   • It reconstructs the exact original phase from magnitude alone
   • A simple baseline for turning magnitude estimates into audio
   • It uses a learned neural vocoder to predict the waveform
   • It guarantees a natural-sounding waveform from any magnitude input
7. How do learned neural vocoders differ from Griffin-Lim?
   • They use no learned parameters and only alternate fixed projections
   • They must reuse Griffin-Lim's estimated phase at every step
   • They only predict transcript words rather than waveform samples
   • They use a trained model to map acoustic representations to waveforms
8. What should be done when judging Griffin-Lim output quality?
   • Listen and evaluate the task-relevant audio, not only a spectral score
   • Assume a smaller spectral error guarantees natural speech
   • Use iteration count alone as the quality measure
   • Judge every recording from one clean reconstruction example