Steganalysis has attracted researchers attention overwhelmingly in last few years which discriminate stego images from non-stego images. The performance of a Steganalysis depends not only on the choice of classifier but also on features that are used to represent the image. Features extracted from images may contain irrelevant and redundant features which makes them inefficient for machine learning. Relevant features not only decrease the processing time to train a classifier but also provide better generalization. In this paper, kullback divergence measure, chernoff distance measure and linear regression are used for relevant feature selection. The performance of steganalysis using different measures used for feature selection is compared and evaluated in terms of classification error and computation time of training classifier. Experimental results show that Linear regression measure used for feature selection outperforms other measures used for feature selection in terms of both classification error and compilation time.
Labeling speech signals is a critical activity that cannot be overlooked in any of the early phases of designing a system based on speech technology. For this, an efficient particle swarm optimization (PSO)-based clustering algorithm is proposed to classify the speech classes, i.e., voiced, unvoiced, and silence. A sample of 10 signal waves is selected, and their audio features are extracted. The audio signals are then partitioned into frames, and each frame is classified by using the proposed PSO-based clustering algorithm. The performance of the proposed algorithm is evaluated using various performance metrics such as accuracy, sensitivity, and specificity that are examined. Extensive experiments reveal that the proposed algorithm outperforms the competitive algorithms. The average accuracy of the proposed algorithm is 97%, sensitivity is 98%, and specificity is 96%, which depicts that the proposed approach is efficient in detecting and classifying the speech classes.
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