Improved Frame-Wise Segmentation of Audio Signals for Smart Hearing Aid Using Particle Swarm Optimization-Based Clustering

Abstract

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.

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks

“…Network classification is a key component of network Intrusion Detection Systems (IDS) [4] , which use categorized network traffic to carry out operations like monitoring, discovering, controlling, and optimizing. Classifying network traffic is a crucial task that helps Internet Service Providers (ISPs) [5] identify the different types of application flows in a network. ISPs or network operators can control a network's overall performance with the aid of network traffic classification [6] .…”