Amplitude of a signal. [-1, 1] vs variants and MFCC implications

1. What is the significance of MFCC?
2. What are the 39 features of MFCC?
3. What is the advantages of MFCC?
4. What features are extracted from MFCC?

What is the significance of MFCC?

MFCCs are commonly used as features in speech recognition systems, such as the systems which can automatically recognize numbers spoken into a telephone. MFCCs are also increasingly finding uses in music information retrieval applications such as genre classification, audio similarity measures, etc.

What are the 39 features of MFCC?

So the 39 MFCC features parameters are 12 Cepstrum coefficients plus the energy term. Then we have 2 more sets corresponding to the delta and the double delta values. Next, we can perform the feature normalization. We normalize the features with its mean and divide it by its variance.

What is the advantages of MFCC?

The advantage of MFCC is that it is good in error reduction and able to produce a robust feature when the signal is affected by noise. SVD/PCA technique is used to extract the important features out of the B-Distribution representation.

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.