How can I modify a sound signal based on it's MFCC

1. What is MFCC in audio?
2. How is MFCC calculated?
3. How many MFCC features are there?
4. How to use MFCC in Python?

What is MFCC in audio?

In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency. Mel-frequency cepstral coefficients (MFCCs) are coefficients that collectively make up an MFC.

How is MFCC calculated?

Derivatives are calculated by taking the difference of these coefficients between the samples of the audio signal and it will help in understanding how the transition is occurring. So overall MFCC technique will generate 39 features from each audio signal sample which are used as input for the speech recognition model.

How many MFCC features are there?

MFCC has 39 features. We finalize 12 and what are the rest. The 13th parameter is the energy in each frame.

How to use MFCC in Python?

MFCC — Mel-Frequency Cepstral Coefficients

mfcc is used to calculate mfccs of a signal. By printing the shape of mfccs you get how many mfccs are calculated on how many frames. The first value represents the number of mfccs calculated and another value represents a number of frames available.