Adaptive Data Hiding Based Telephony Speech Enhancement

Public telephone systems transmit speech across a limited frequency range, about 300–3400 Hz, called narrowband (NB) which results in a significant reduction of quality and intelligibility of speech. This paper proposes a fully backward compatible novel method for bandwidth extension of NB speech. The method uses adaptive data hiding technique to provide a perceptually better wideband speech signal. The spectral envelope parameters are extracted from the high frequency components of speech signal existing above NB, which are then spread by using spreading sequences, and are embedded in the NB speech signal using adaptive data hiding technique. The embedded information is extracted at the receiving end to reconstruct the wideband speech signal. Theoretical and simulation analyses show that the proposed method is robust to quantization and channel noises. The log spectral distortion test clearly show that the reconstructed wideband signal gives a much better performance in terms of speech quality when compared to the conventional speech bandwidth extension methods employing data hiding.

Countering DDoS Attacks in SIP Based VoIP Networks Using Recurrent Neural Networks

Many companies have transformed their telephone systems into Voice over IP (VoIP) systems. Although implementation is simple, VoIP is vulnerable to different types of attacks. The Session Initiation Protocol (SIP) is a widely used protocol for handling VoIP signaling functions. SIP is unprotected against attacks because it is a text-based protocol and lacks defense against the growing security threats. The Distributed Denial of Service (DDoS) attack is a harmful attack, because it drains resources, and prevents legitimate users from using the available services. In this paper, we formulate detection of DDoS attacks as a classification problem and propose an approach using token embedding to enhance extracted features from SIP messages. We discuss a deep learning model based on Recurrent Neural Networks (RNNs) developed to detect DDoS attacks with low and high-rate intensity. For validation, a balanced real traffic dataset was built containing three attack scenarios with different attack durations and intensities. Experiments show that the system has a high detection accuracy and low detection time. The detection accuracy was higher for low-rate attacks than that of traditional machine learning.

Expanding Role of Telephone Systems in Healthcare

The telephone systems in healthcare settings serve as a viable tool for improving the quality of service provided to patients, decreasing the cost, and improving the patient satisfaction. It can play a pivotal role for transformation of the healthcare delivery for embracing personalized and patient centered care. This chapter presents a systematic review of new developments of healthcare telephone system operations in various areas such as tele-health. Current research on topics such as tele-diagnosis, tele-nursing, tele-consultation is outlined. Specific issues associated with the emerging applications such as underreferral, legal issues, patient acceptance, on-call physician are discussed. Meanwhile, the architecture and underlying technologies for healthcare telephone systems are introduced, and the performance metrics for measuring the system operations are provided. In addition, challenges and opportunities related with improving the healthcare telephone systems are identified, and the potential opportunities of optimizing these systems are pointed out.

Introduction

This introductory chapter provides an overview of signal traffic in the contemporary era of media globalization—an era characterized by contradictory global mediascapes and multiple media infrastructures. Signal traffic refers to the movement of electronic media across various parts of the planet. Today, broadcasting, cable, satellite, Internet, and mobile telephone systems are used simultaneously, and sometimes in coordinated ways, to route signal traffic to and from sites around the world. The content and form of contemporary media—whether television programs or online games—are shaped in relation to the properties and locations of these distribution systems. As a suggestive concept, then, signal traffic demarcates a critical shift away from the analysis of screened content alone and toward an understanding of how content moves through the world and how this movement affects content's form.