NIDA: A Parametric Vocal Quality Assessment Algorithm over Transient Connections

  • Authors:
  • Sofiene Jelassi;Habib Youssef;Lingfen Sun;Guy Pujolle

  • Affiliations:
  • Research Unit PRINCE, ISITCom, Hammam Sousse, Tunisia and Laboratory of Computer Science (LIP6), University of Pierre and Marie Curie, Paris, France;Research Unit PRINCE, ISITCom, Hammam Sousse, Tunisia;School of Computing, Communications and Electronics, University of Plymouth, UK;Laboratory of Computer Science (LIP6), University of Pierre and Marie Curie, Paris, France

  • Venue:
  • MMNS 2009 Proceedings of the 12th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services: Wired-Wireless Multimedia Networks and Services Management
  • Year:
  • 2009

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Abstract

This paper presents NIDA, a Non-Intrusive Disconnection-aware vocal quality assessment Algorithm. NIDA accurately estimates vocal perceived quality over wireless data networks by discriminating the perceptual effect of a single random packet loss, 2-4 consecutive packet losses (burst) stemming from contentions, and discontinuity entailed by transient loss of connectivity. NIDA properly accounts for transient loss of connectivity experienced by mobile users over wireless data networks, stemming from vertical and horizontal handovers, or when users roam out of the coverage area of the associated infrastructure. To this end, a novel lossy wireless data channel model has been conceived based on a continuous-time Markov model. The channel model is calibrated at run-time based on a set of measurements gathered at packet layer using the header content of received voice packets. The perceived quality under each state is properly quantified, then combined in order to predict quality degradation due to wireless data channel features. Performance evaluation study shows that quality degradation ratings calculated using NIDA strongly correlate with quality degradation ratings calculated based on ITU-T PESQ intrusive algorithm, which mimics tightly subjective human rating behavior.