Prueba de clasificación de audio de YAMNet

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿Qué predice YAMNet según la descripción de su modelo documentado?
   • Verified identities of individual speakers
   • Word-level transcripts for every recording
   • Audio event classes from the AudioSet vocabulary
   • Exact musical-note scores for every recording
2. ¿Qué puede ofrecer YAMNet además de las puntuaciones de las clases?
   • Verified annotations that replace a labeled evaluation set
   • Intermediate audio embeddings
   • Speaker identities that remain verified across recordings
   • A new project-specific class ontology without any adaptation
3. ¿Por qué YAMNet puede no expresar la etiqueta muy específica de un proyecto?
   • Every audio input must use the recording's original sample rate unchanged
   • It outputs only reconstructed waveforms rather than scores
   • An embedding makes every unsupported class directly available as a named output
   • Its output vocabulary is limited to its trained class taxonomy
4. What does a frame-level score represent?
   • A score associated with a model window, not automatically a final clip label
   • A verified label that necessarily applies to the entire recording
   • A count of every event throughout the recording
   • A guarantee that the highest class is the only audible event
5. ¿Por qué cambiar un campo de metadatos de frecuencia de muestreo por sí solo no es suficiente para el remuestreo?
   • It automatically converts every stereo signal to mono
   • It changes the number of AudioSet output classes
   • It does not recompute the waveform at the new sampling rate
   • It removes the need to check sample values and timing
6. ¿Cómo pueden los equipos posteriores reutilizar las incorporaciones de YAMNet?
   • Replace all target-task evaluation with pretraining scores
   • Train a smaller classifier for project-specific labels
   • Assume all target labels are already distinguished by the same score columns
   • Guarantee that downstream probabilities are calibrated without testing
7. What does an AudioSet score establish about a new deployment environment?
   • Guaranteed accuracy in that setting
   • A calibrated project-specific probability
   • That all event categories are represented
   • A model output that should be validated on that domain
8. Which split helps avoid leakage when recordings share a session or source?
   • Group recordings by source or session before partitioning
   • Shuffle derived crops across every split
   • Put one copy in train and another in test
   • Split only the model's output scores