MFCCs and mean normalization

1. What do MFCCs represent?
2. What is MFCC algorithm?
3. What are MFCC features?
4. What is the advantages of MFCC?

What do MFCCs represent?

The mel frequency cepstral coefficients (MFCCs) of a signal are a small set of features (usually about 10-20) which concisely describe the overall shape of a spectral envelope. In MIR, it is often used to describe timbre.

What is MFCC algorithm?

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 MFCC features?

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.

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.