How to train and test deep neural network using MFCC features?

1. What is MFCC in deep learning?
2. What features are extracted from MFCC?
3. Is MFCC a learning machine?
4. Why is MFCC used for feature extraction?

What is MFCC in deep learning?

These coefficients, called mel-frequency cepstral coefficients (MFCCs), are the final features used in many machine learning models trained on audio data!

What features are extracted from MFCC?

The MFCC feature extraction technique basically includes windowing the signal, applying the DFT, taking the log of the magnitude, and then warping the frequencies on a Mel scale, followed by applying the inverse DCT.

Is MFCC a learning machine?

(1) MFCC (Mel-Frequency Cepstral Coefficients):

A.k.a 'Most-frequently considered coefficients', MFCC is that one feature you would see being used in any machine learning experiment involving audio files.

Why is MFCC used for feature extraction?

It is observed that extracting features from the audio signal and using it as input to the base model will produce much better performance than directly considering raw audio signal as input. MFCC is the widely used technique for extracting the features from the audio signal.