Aural segmant scrutiny framework pestial on aspect mining speech-segement scrutinity: Feature extraction

My research work address dilemma of categorization of uninterrupted general aural data for content based recovery. This research article deals with scheme for classifying aural data Segmentation is also done on same data so that processing rate is faster. Aural data is able to classify into eight categories simple speech, noise, silence, music, single speech with music, double speech with music, speech without music, instrument sound. There are so many features are there, among that linear prediction coefficient, Mel-frequency campestral coefficients etc. We studied all possible features. Depending upon Campestral based features which provide accurate classification. To reduce errors aural segmentation is done. So that processing rate is faster & to get more accuracy. There are so many features are there, among that linear prediction coefficient, Mel-frequency Cepstral coefficients etc. We studied all possible features. Depending upon Cepstral based features which provide accurate classification. To reduce errors aural segmentation is done. So that processing rate is faster & to get more accuracy