Audio Stream Analysis for Call Priority Ranking
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Solution Overview
Problem
Current call prioritization methods in call centers and conferencing systems are inadequate as they often rely on energy levels, which can be misleading due to background noise, and fail to distinguish between voice and non-voice sound energy, leading to inaccurate prioritization and user experience degradation.
Innovation Solution
An audio stream analysis method that identifies and prioritizes calls based on specific audio characteristics, such as urgency words, spectral features, and background noise patterns, while distinguishing between voice and non-voice energy to determine the priority and access rights of speakers in conferencing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If call prioritization is based on energy levels (loudest speakers), then the system can automatically prioritize calls, but the prioritization becomes inaccurate when background noise or environmental factors cause false positives
Solution Approach 1:
The audio stream is segmented into multiple frequency bands using spectral analysis. By dividing the audio spectrum into distinct bands (e.g., low, mid, high frequencies), the system can analyze voice characteristics separately from background noise patterns, enabling more accurate prioritization that isn't misled by overall energy levels alone.
Solution Approach 2:
Spectral frequency analysis acts as an intermediary between the raw audio signal and the prioritization decision. Instead of directly using energy levels, the system introduces spectral analysis as an intermediate step that filters out false positives by identifying voice-specific frequency patterns before prioritization occurs.
2Measurement precision
If the system analyzes audio streams to distinguish voice from background noise, then prioritization accuracy improves, but the system complexity increases
Solution Approach 1:
The system transforms the audio signal from the time domain to the frequency domain by changing parameters (time → frequency representation). This parameter transformation enables voice and noise distinction through spectral characteristics rather than requiring complex temporal analysis algorithms, reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The patent replaces complex signal processing mechanisms with spectral frequency analysis. Instead of using complicated temporal pattern recognition or machine learning models, the system substitutes a more straightforward spectral analysis approach that leverages the natural frequency characteristics of human voice versus background noise.
Data Source
AI summary
A method and system of prioritizing calls based on audio stream analysis includes receiving a plurality of calls, wherein each call comprises an audio stream. The audio stream associated with one of the calls is analyzed for pre-determined audio characteristics. The call is processed based on the audio characteristics of the call. A system for prioritizing calls includes a multipoint control unit for receiving calls. An audio stream capture system captures an audio stream from the calls. The audio stream is analyzed by the capture system according to one or more selected criteria and an urgency priority ranking is determined for each call. The calls are ranked in a queue database according to urgency priority. A controller manages the audio stream capture system, the audio analyzer and queue database computer system.