Enhanced VoIP by Signal Reconstruction and Voice Quality Assessment

Abstract

The Internet and its packet based architecture is becoming an increasingly ubiquitous communications resource, providing the necessary underlying support for many services and applications. The classic voice call service over fixed circuit switched networks suffered a steep evolution with mobile networks and more recently another significant move is being witnessed towards packet based communications using the omnipresent Internet Protocol (IP) (Zourzouvillys & Rescorla, 2010). It is known that, due to real time requirements, voice over IP (VoIP) needs tighter delivery guarantees from the networking infrastructure than data transmission. While such requirements put strong bounds on maximum end to end delay, there is some tolerance to errors and packet losses in VoIP services providing that a minimum quality level is experienced by the users. Therefore, voice signals delivered over IP based networks are likely to be affected by transmission errors and packet losses, leading to perceptually annoying communication impairments. Although it is not possible to fully recover the original voice signals from those received with errors and/or missing data, it is still possible to improve the quality delivered to users by using appropriate error concealment methods and controlling the Quality of Service (QoS) (Becvar et al., 2007). This chapter is concerned with voice signal reconstruction methods and quality evaluation in VoIP communications. An overview of suitable solutions to conceal the impairment effects in order to improve the QoS and consequently the Quality of Experience (QoE) is presented in section 2. Among these, simple techniques based on either silence or waveform substitution and others that embed voice parameters of a packet in its predecessor are addressed. In addition, more sophisticated techniques which use diverse interleaving procedures at the packetization stage and/or perform voice synthesis at the receiver are also addressed. Section 3 provides a brief review of relevant algebra concepts in order to build an adequate basis to understand the fundamentals of the signal reconstruction techniques addressed in the remaining sections. Since signal reconstruction leads to linear interpolation problems defined as system of equations, the characterization of the corresponding system matrix is necessary because it provides relevant insight about the problem solution. In such