I have an audio recording (wav file) that is 2 seconds long. That is my sample and it needs to be classified as [class_A] or [class_B].
To extract MFCC features, I divided the sample into frames (183 frames to be exact) and I've gotten MFCCs (13 coefficients) from each frame.
Now I have 183 feature vectors, the length of each vector is 13.
My question is; how exactly do you use those vectors with classifier (k-NN or SVM for example)? I have 183 vectors that represent 1 sample. I know how to work with 1 vector for 1 sample, but I don't know what to do if I have 183 of them.
Should I concatenate the MFCC vectors from each audio file to transform a matrix of 13x182 to 1x2366 (so I have 2366 MFCC features for every audio file instead of 13 for every frame?), should I use the mean ?
Apologize in advance for any possible obvious explanation that I missed.