Mel-Frequency Cepstral Coefficients (MFCC) Extraction in Speech Processing

Mel-Frequency Cepstral Coefficients (MFCC) extraction is a fundamental technique in speech signal processing. This MATLAB implementation demonstrates how to effectively extract key acoustic features from speech signals. MFCCs are numerical representations that capture the spectral characteristics of speech signals, widely used in applications such as speech recognition, speaker identification, and speech synthesis. The algorithm typically involves several key steps: pre-emphasis to enhance high frequencies, framing the signal into short segments, windowing to reduce spectral leakage, computing the Fast Fourier Transform (FFT) to obtain spectral information, applying Mel-filter banks to simulate human auditory perception, taking logarithms of filter bank energies, and finally performing Discrete Cosine Transform (DCT) to decorrelate the features and obtain cepstral coefficients. This implementation provides a practical approach to extract these important parameters, which can serve as input features for various machine learning models in speech technology applications.