Drawbacks of existing segmentation, classification and feature extraction approaches | Merits of our proposed work |
・ It can be applied only on discrete audio segments. ・ Generation of extra overhead during the computation of MFCC features. ・ Distinguishing of the speech from the music signals is poor. ・ The accuracy of the existing classification techniques is low. ・ High computational complexity and cost. | ・ The clear features of the speech signal filtered from other background signals are obtained. ・ PNN-based classification provides a better prediction of the classified label using the probability estimation. ・ The presence of silence and irrelevant frequency details in the audio signal is eliminated. ・ PNN classification approach achieves efficient classification of the musical and non-musical signal. ・ The accuracy of the proposed approach is high. |