Cloud VoIP Voice Quality Monitoring via Real-Time MOS Analysis
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Solution Overview
Problem
In cloud-based VoIP systems, voice quality issues are challenging to diagnose and resolve due to the complexity of the network, where voice media traverses multiple appliances, making it difficult to localize and address degradation sources in real-time, and existing methods often rely on post-call data analysis which may not reproduce the problem accurately.
Innovation Solution
Implement a system that monitors live active calls for voice quality degradations, using dynamic mean opinion score (MOS) estimates and time series database analysis to identify and alert on issues, with the ability to automatically debug and capture data during calls, employing star-codes for user-initiated debugging and automated root cause analysis without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If post-call data analysis is used to diagnose voice quality issues, then diagnostic accuracy may be improved, but the response time and ability to resolve issues in real-time deteriorates
Solution Approach 1:
The system performs preliminary voice quality monitoring and analysis during the active call rather than waiting for post-call data collection. The monitoring device continuously captures voice media streams and computes MOS scores in real-time, enabling immediate detection of quality degradation before the call ends.
Solution Approach 2:
The system implements continuous feedback loops where voice quality metrics are monitored in real-time, compared against thresholds, and trigger immediate alerts or automated debugging actions. This feedback mechanism enables dynamic adjustment and immediate response to quality issues during the call.
2Reliability
If comprehensive voice quality monitoring is implemented during active calls, then the ability to identify and resolve issues in real-time is improved, but system complexity and computational resources required deteriorates
Solution Approach 1:
The monitoring system is segmented into independent functional modules: voice media stream capture, MOS score computation, threshold comparison, alert generation, and automated debugging. Each module operates independently, allowing the system to scale and manage complexity through modular architecture.
Solution Approach 2:
A dedicated monitoring device acts as an intermediary between the VoIP network and the quality analysis functions. This intermediary captures voice media streams, performs local preprocessing and MOS computation, and only transmits essential quality metrics and alerts to external systems, reducing overall system complexity.
3Productivity
If automated debugging and root cause analysis are implemented, then the speed of problem resolution is improved, but the complexity of the monitoring system deteriorates
Solution Approach 1:
The monitoring system performs self-service through automated debugging capabilities that automatically capture call data, analyze root causes, and generate diagnostic reports without human intervention. The system autonomously executes debugging sequences, collects relevant metrics, and identifies problem sources, reducing the need for manual analysis.
Solution Approach 2:
The system pre-configures debugging templates, analysis algorithms, and root cause determination logic before issues occur. When quality degradation is detected, these pre-prepared resources are immediately deployed, enabling rapid automated diagnosis without requiring complex real-time decision-making infrastructure.
4Reliability
If real-time voice quality monitoring is implemented in cloud-based VoIP systems, then customer satisfaction is improved, but the computational resources and energy consumption deteriorates
Solution Approach 1:
The system monitors only the essential voice quality parameters needed for customer satisfaction (MOS score, packet loss, jitter) rather than comprehensively analyzing all possible call metrics. Threshold-based alerting triggers detailed analysis only when necessary, avoiding continuous full-scale monitoring and reducing computational overhead.
Solution Approach 2:
The monitoring system extracts only the critical quality metrics from the voice media stream (MOS score, network performance indicators) and separates these from the actual voice content. This extraction approach enables quality monitoring with minimal computational resources by focusing only on essential parameters rather than processing the entire audio stream.
Data Source
AI summary
Systems and methods for communications are disclosed. The systems and methods can monitor a cloud-based voice over internet protocol (VoIP) calling system to determine an active call. The systems and methods can also analyze the active call to determine an indication of call quality, the analyzing occurring during the active call. Additionally, the systems and methods can compare the indication of call quality to a quality threshold. The compare can occur during the active call to determine when the active call has a poor call quality. The systems and methods can also report the poor call quality based on the comparing the indication of call quality to the quality threshold.