I am building CNN and SVM models which take in MFCC features as input. The MFCC matrices shape is (13, n). The 13 rows are coefficients and n columns represent n time frames. So each row in the matrix is a representation of the value of the particular coefficient over different time frames. I am not sure how to normalise this matrix. Should it be rowwise (normalize a single coefficient over all the time frames) or columnwise (normalize all the coefficients in a single time frame).
[ [1.08, 8.97, 78.7, ........], [1.08, 8.97, 78.7, ........], [1.08, 8.97, 78.7, ........], . . . [19.8, 7.65, 76.5, ........] ]
Currently I am using
sklearn, but I am not sure if its the right thing to do.