I am working on pipelines for audio processing (audio features that will go into a neural net to create some embeddings).
We are using two features : Filter banks, or MFCC.
I was wondering what was the good approach with normalization :
Should we normalize those frames on each dimension (each frequency / mfcc coefficient), or normalize across all dimensions ?
This could lead to important differences in the relative importance of features .