抽象的な

COMPARISON OF ENHANCED SCHEMES FOR AUDIO CLASSIFICATION

Dr. V. Radha and G.Anuradha

In the modern era of communication, audio plays an important role in understanding a digital media. Due to the rise of economical audio capturing devices, the amount of audio data available both online and offline is enormous and techniques that can automatically classify and retrieve these audio data is an immediate need. An automatic content based audio classification and retrieval system consists of three modules namely, feature extraction, classification and retrieval. This paper presents a comparative study of two algorithms that performs these three steps in different manners. The performance of the selected systems are analyzed while using four different features (acoustic, perceptual, mel-frequency cepstral coefficients (MFCC) and a combination of perceptual and MFCC) and four classifiers that enhanced Support Vector Machine (SVM) and Centroid Neural Network (CNN) along with its base versions, SVM and CNN. Experimental results showed that the enhanced SVM algorithm when using the combined feature vector produced improved accuracy and reduced error rate.