Compare audio preprocessing and training

Imagine you are developing an audio model for a voice assistant. Contrast widely used audio data preprocessing methods and describe their trade-offs. In particular, cover options like raw waveform input versus engineered features, normalization, segmentation, and data augmentation. Next, outline the specific end-to-end steps you would take to train the model, from preparing data through evaluation.

Overview: This question tests knowledge of audio data preprocessing and end-to-end model training, covering skills in feature representation choices, normalization, segmentation, augmentation, and pipeline orchestration for audio machine learning.