Voice Call Hold Detection Through Automated Audio Monitoring
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Solution Overview
Problem
Existing voice communication systems require users to actively monitor calls on hold, leading to increased power consumption and user intervention, which can drain battery life and be inconvenient.
Innovation Solution
An on hold client monitors audio streams to automatically detect the end of hold status, using machine learning models to identify human voices and respond with solicitation signals, reducing the need for user intervention and conserving device resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users actively monitor calls on hold by increasing volume, activating speakerphone, and repeatedly activating the screen, then the caller can determine when a service representative becomes active, but power consumption increases and battery life is drained
Solution Approach 1:
The system performs self-service by automatically monitoring the audio stream to detect when a service representative becomes active. The on hold client autonomously analyzes audio characteristics, identifies transitions from hold music to human speech, and determines representative availability without requiring user intervention. This eliminates the need for users to manually increase volume, activate speakerphone, or repeatedly wake the screen, thereby resolving the contradiction between reliable call monitoring and power consumption.
2Reliability
If users actively monitor calls on hold by making multiple inputs at the client device, then the caller can determine when a service representative becomes active, but the number of user inputs required increases
Solution Approach 1:
The on hold client autonomously performs call monitoring by analyzing the audio stream to detect representative availability. The system automatically identifies when hold music transitions to human speech, processes the audio characteristics, and determines when a service representative is active. This self-service approach eliminates the need for users to make multiple inputs such as activating the screen, adjusting volume, or switching speakerphone modes, thereby resolving the contradiction between monitoring reliability and ease of operation.
Solution Approach 2:
The system replaces manual mechanical user actions (screen activation, volume adjustment, speakerphone switching) with automated audio processing and analysis. The on hold client uses audio stream analysis, speech detection algorithms, and machine learning models to automatically determine representative availability, substituting the mechanical user interaction system with an automated acoustic monitoring system. This resolves the contradiction by eliminating frequent user inputs while maintaining accurate call monitoring.
3Reliability
If the client device screen is repeatedly activated to monitor the call status, then the caller can ensure the call is still active and on hold, but power consumption increases
Solution Approach 1:
The system replaces the mechanical action of repeatedly activating the screen with automated audio stream analysis. The on hold client continuously monitors the audio characteristics to verify call status and detect representative availability without requiring the display to be active. By substituting visual monitoring (screen activation) with acoustic monitoring (audio stream analysis), the system maintains reliable call status verification while eliminating the high power consumption associated with frequent screen wake-ups.
Data Source
AI summary
Automated monitoring of a voice communication session, when the session is in an on hold status, to determine when the session is no longer in the on hold status. When it is determined that the session is no longer in the on hold status, user interface output is rendered that indicates that the on hold status of the session has ceased. In some implementations, an audio stream of the session can be monitored to determine, based on processing of the audio stream, a candidate end of the on hold status. In response, a response solicitation signal is injected into an outgoing portion of the audio. The audio stream can be further monitored for a response (if any) to the response solicitation signal. The response (if any) can be processed to determine whether the end of the on hold status is an actual end of the on hold status.