Voice Quality Monitoring with FEC-Aware Packet Loss Estimation
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Solution Overview
Problem
Existing voice quality monitoring systems in packet-based voice communication systems suffer from low accuracy in estimating voice quality due to the lack of consideration for forward error correction (FEC) in statistical metrics, leading to increased prediction errors and degraded call quality.
Innovation Solution
A method to modify statistical metrics related to lost voice packets by incorporating recovery data from FEC, which allows for the reduction of prediction errors in voice quality estimates by accounting for recovered packets and their recovery factors, thereby improving the accuracy of voice quality estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If forward error correction (FEC) is used to recover lost voice packets, then voice quality is improved, but the accuracy of voice quality estimation is degraded due to lack of FEC consideration in statistical metrics
Solution Approach 1:
The system introduces feedback by incorporating FEC recovery data into the statistical metrics calculation. The voice quality monitoring system receives information about recovered packets and uses this feedback to adjust the packet loss rate and other statistical metrics, thereby improving estimation accuracy while accounting for FEC's impact on voice quality
Solution Approach 2:
The system changes the parameters used in voice quality estimation by introducing new metrics that factor in FEC recovery. Instead of using only raw packet loss statistics, the system modifies parameters like packet loss rate to reflect actual voice quality impact after FEC recovery, resolving the contradiction between FEC benefits and estimation accuracy
2Measurement precision
If statistical metrics are modified to account for FEC recovery data, then voice quality estimation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-defining the relationship between FEC recovery data and statistical metrics. Recovery factors and adjustment rules are established in advance, allowing the system to automatically modify metrics without complex real-time calculations, thus improving accuracy while limiting complexity growth
Solution Approach 2:
The patent introduces an intermediary component that sits between FEC processing and voice quality estimation. This intermediary module translates FEC recovery data into adjusted statistical metrics, simplifying the overall system architecture by creating a clear interface layer that handles the complexity of metric modification
Data Source
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AI summary
This disclosure falls into the field of voice communication systems, more specifically it is related to the field of voice quality estimation in a packet based voice communication system. In particular the disclosure provides methods, computer program products and devices for reducing a prediction error of the voice quality estimation by considering forward error correction of lost voice packets.