Feature Selection and Extraction of Audio Signal

In the feature extraction at the signal level three feature extraction methods were used, statis- tical in time and frequency domain, wavelet package analysis and complex

The proposed feature extraction method consists of an adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm for effective extraction of

Here we have discussed some mostly used feature extraction techniques like Mel frequency Cepstral Co-efficient (MFCC), Linear Predictive Coding (LPC) Analysis,

By using MFCC algorithm feature is extracted from which we can observe how changes occur in pitch, frequency and other features when emotion changes .We have done Frame

Based on the feature selection, two new characteristics of speech emotion, MFCC feature extracted from the fundamental frequency curve (MFCCF0) and amplitude perturbation

Keywords —Acoustic Phonetic Approach, Artificial Intelligence, Feature extraction, LPC, MFCC, Pattern Recognition Approach, Speech Recognition, Word Error Rate..

By studying the techniques used in the audio feature extraction process, the proposed techniques are hoped to provide a better performance of audio classification accuracy

The system are firstly preprocessed the audio data, theses audio samples are extracted the timbre features (zcr, centroid, shew, centroid kurtosis bandwidth,