Practical handling.
WHAT IMAGE PREPROCESSING INVOLVES
Resizing to the model's expected dimensions Normalising pixel values as the model expects Handling colour channel order
WHY MATCHING THE MODEL MATTERS
Pre-trained models expect specific normalisation, and using different values degrades performance silently.
WHAT TO CHECK
The preprocessing used during the model's original training.
WHAT AUDIO PREPROCESSING INVOLVES
Resampling to a consistent rate Converting to a spectral representation Normalising loudness
WHAT A SPECTROGRAM IS
A representation of frequency content over time.
WHY IT IS USED
It turns audio into something image-like, which convolutional and transformer models handle well.
WHAT TO BE CAREFUL WITH
Sample rate mismatches, which distort everything downstream Clipping from normalisation Silence and noise segments
WHAT AUGMENTATION LOOKS LIKE FOR AUDIO
Noise addition, time shifting, speed variation, masking of frequency bands.
WHAT DATA VOLUME LOOKS LIKE
Large, which affects storage, transfer and training time.
WHAT TO DO ABOUT IT
Preprocess once and store the result, rather than repeating per epoch.
WHAT TO VERIFY
That a sample of processed data still looks and sounds correct.
