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Augmenting Speech Quality Estimation in Software-Defined Networking Using Machine Learning Algorithms.

Jan Rozhon1, Filip Rezac1, Jakub Jalowiczor2

  • 1Deparment of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2172/15, 708 00 Ostrava, Czech Republic.

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|June 2, 2021
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Summary
This summary is machine-generated.

Software-Defined Networking (SDN) enables dynamic speech quality measurement by analyzing network flow statistics. This approach allows for real-time adjustments to ensure high-quality multimedia communications and user experience.

Keywords:
OpenFlowartificial neural networkssoftware defined networksspeech analysis

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Area of Science:

  • Computer networking
  • Multimedia communications
  • Network management

Background:

  • Software-Defined Networking (SDN) enhances data center agility and flexibility.
  • Modern internet traffic predominantly comprises multimedia services, necessitating high communication quality.
  • Existing network management tools have limitations in fully leveraging SDN for quality monitoring.

Purpose of the Study:

  • To explore the potential of SDN for effective network management in multimedia communication.
  • To develop a method for estimating speech communication quality using SDN controller data.
  • To investigate the integration of latency characteristics for improved quality assessment.

Main Methods:

  • Utilizing SDN switches to monitor flow statistics (packets, bytes, loss).
  • Employing PacketIn and Multipart messages for data retrieval by the SDN controller.
  • Incorporating dummy packet injection and RTCP analysis for latency measurement.
  • Implementing a Convolutional Neural Network (CNN) model for speech quality estimation.

Main Results:

  • A novel method for dynamic measurement of speech quality based on individual RTP stream statistics.
  • The ability to dynamically adjust network routing for individual calls when speech quality declines.
  • A CNN-based model that accounts for delay, outperforming traditional PESQ/POLQA and E-model in accuracy.

Conclusions:

  • SDN offers a powerful platform for real-time monitoring and management of multimedia communication quality.
  • The proposed method enables proactive network adjustments to maintain high Quality of Service (QoS) and Quality of Experience (QoE).
  • The CNN model represents a significant advancement in accurately measuring speech quality in dynamic network conditions.