MFCC Feature Extraction for Speech Emotion Recognition
MFCC Feature Extraction for Speech Parameters with Emotion Differentiation Capabilities and Algorithm Implementation Insights
Speech Feature Extraction Using Gammatone Filters
GFCC (Gammatone Frequency Cepstral Coefficients) employs gammatone filterbanks for speech feature extraction, implementing auditory-inspired frequency analysis through optimized filter design and spectral processing.
MFCC - Mel-Frequency Cepstral Coefficients
MFCC, or Mel-Frequency Cepstral Coefficients, represent one of the fundamental features in speech signal processing that effectively models human auditory perception. The computational pipeline involves preprocessing, windowing, Fourier transformation, power spectrum calculation, natural logarithm application, and discrete cosine transform (DCT). The MATLAB implementation leverages a speech processing toolbox available for online download, with key functions including frame segmentation, FFT operations, and Mel-filterbank integration.
BP Neural Network Classification for Speech Features
BP Neural Network Speech Feature Classification with Implementation Details