ML Audio Quality Detection for Video Conference Troubleshooting
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Solution Overview
Problem
Existing video conferencing systems rely on subjective verbal feedback for audio quality issues, lacking quantitative measures and the ability to track audio quality over time, leading to inefficient troubleshooting and systemic problems.
Innovation Solution
Implementing a machine learning model to measure audio quality, compute a metric for low-quality audio, and generate notifications or corrective actions based on predetermined thresholds, enabling quantitative assessment and systemic issue detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective verbal feedback is used for audio quality assessment, then the system is simple to implement, but the measurement precision and ability to track audio quality over time is insufficient
Solution Approach 1:
The patent replaces the mechanical/manual system of subjective verbal feedback with an automated machine learning-based audio analysis system. The ML model processes audio streams to generate quantitative quality metrics, eliminating the need for human operators to manually assess and report audio issues, thereby achieving precise automated measurement while maintaining operational simplicity
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the audio stream and the quality assessment process. This ML intermediary automatically analyzes audio characteristics and generates objective quality metrics, bridging the gap between raw audio data and actionable quality insights without requiring direct human intervention
2Productivity
If manual troubleshooting is performed for audio quality issues, then the troubleshooting process can be performed with simple tools, but the productivity and efficiency of resolving audio issues is low
Solution Approach 1:
The patent performs preliminary actions by continuously monitoring and analyzing audio quality in real-time before issues become critical. The system proactively detects audio quality degradation and generates early warnings, allowing troubleshooting to begin before complete failure occurs, thereby reducing overall resolution time and improving productivity
Solution Approach 2:
The patent implements automated feedback loops where the ML model continuously analyzes audio streams, compares quality metrics against thresholds, and provides real-time feedback on audio quality status. This automated feedback system eliminates delays associated with manual checking and enables rapid identification and resolution of audio issues
3Reliability
If audio quality monitoring is implemented continuously, then the reliability of audio quality assessment is improved, but the energy consumption increases
Solution Approach 1:
The patent implements periodic action by analyzing audio quality at strategically selected intervals rather than continuously processing every audio sample. The ML model periodically assesses audio streams at key moments and uses change detection algorithms to identify significant quality variations, maintaining reliable monitoring while reducing computational energy consumption through interval-based analysis
Data Source
AI summary
Techniques for low-quality audio detection are provided. In an example method, a computing system joins a first client device to a first video conference including a number of connected client devices. The computing system receives, from the first client device, a first audio stream. The computing system determines, using a machine learning (“ML”) model, at least one first audio quality measurement based on the first audio stream. The computing system computes a first metric for the first audio stream based on the at least one first audio quality measurement. In response to the first metric satisfying a predetermined threshold, the computing system outputs a message including first information about a first low-quality audio status associated with the first audio stream.