Push-To-Talk Audio Buffering With AI Message Prioritization
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Solution Overview
Problem
In push-to-talk (PTT) systems, multiple users speaking simultaneously can lead to audio loss, and users speaking before connection establishment causes crucial information to be missed, while existing techniques fail to effectively manage overlapping speech and ensure all audio is captured and transmitted.
Innovation Solution
A server-based AI engine determines importance scores for audio messages using convolutional neural networks (CNN) for nonverbal features and transformer-based AI for verbal features, reordering and buffering messages to ensure critical information is prioritized and transmitted.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time audio transmission is implemented in PTT systems, then communication speed is improved, but audio loss occurs when multiple users speak simultaneously
Solution Approach 1:
The system performs preliminary actions by buffering audio messages before transmission and using AI to predict importance in advance. The server buffers audio from multiple users and uses AI models to predict which messages are most important before transmitting them, preventing audio loss from overlapping speech.
Solution Approach 2:
The system implements feedback mechanisms where the server receives audio messages, analyzes them using AI models that provide importance scores, and uses this feedback to dynamically reorder and prioritize transmission. This feedback loop ensures that critical audio information is transmitted first, preventing loss from simultaneous speech.
2Loss of time
If audio messages are transmitted immediately upon connection, then response time is reduced, but crucial information is missed due to connection establishment delays
Solution Approach 1:
The system performs preliminary action by capturing and buffering audio messages during the connection establishment period before the actual transmission begins. This ensures that crucial information spoken during connection setup is not lost, while still maintaining fast response time through prioritized transmission of buffered messages.
Solution Approach 2:
The server acts as an intermediary that receives audio messages during connection establishment, buffers them temporarily, and then transmits them prioritized by AI importance analysis. This intermediary buffering mechanism prevents information loss during the transition period without significantly increasing response time.
3Reliability
If all audio messages are transmitted equally, then communication completeness is improved, but important messages are not prioritized
Solution Approach 1:
The system applies local quality by differentiating the treatment of different audio messages based on their importance. The AI analysis assigns different priorities to different messages, and the transmission system treats them accordingly - critical messages are transmitted immediately while less important messages are buffered and transmitted later, improving both completeness and efficiency.
Solution Approach 2:
The system changes the parameter of message priority based on AI importance scores. By analyzing audio messages and assigning importance parameters, the system dynamically adjusts transmission timing and priority levels, ensuring that critical information is transmitted efficiently while maintaining communication completeness for all messages.
4Productivity
If AI importance analysis is performed on all audio messages, then message prioritization is improved, but processing time increases
Solution Approach 1:
The system performs AI importance analysis as a preliminary action during the buffering phase, before transmission occurs. By analyzing messages upfront and assigning importance scores, the system enables fast prioritized transmission without adding significant delay, as the analysis is completed in advance during the connection setup and buffering period.
Data Source
AI summary
A push-to-talk server receives, while audio is being played back at a push-to-talk client device, one or more audio messages. The push-to-talk server stores the one or more audio messages in a buffer at the push-to-talk server in an order determined based at least in part on an initiation time of the one or more audio messages. The push-to-talk server determines, using an artificial intelligence engine and based on nonverbal features, an importance score for at least a portion of the one or more audio messages in the buffer. The push-to-talk server reorders the one or more audio messages based on the importance score. The push-to-talk server transmits the one or more audio messages for playback at the push-to-talk client device based on the order of the one or more audio messages.