Push-To-Talk Audio Buffering for Missed Speech and Message Priority
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Solution Overview
Problem
In push-to-talk (PTT) systems, multiple users speaking simultaneously can lead to audio loss, and users pressing the PTT button before connection establishment causes initial audio to be missed, while existing techniques fail to capture and prioritize important messages effectively.
Innovation Solution
Implementing an AI engine at the server to determine importance scores for audio messages based on nonverbal and verbal features, using a convolutional neural network (CNN) or transformer-based models, and buffering messages to reorder and transmit them based on importance, ensuring crucial information is not lost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple users speak simultaneously in PTT systems, then communication flexibility is improved, but audio loss occurs
Solution Approach 1:
The system performs preliminary actions by capturing and buffering audio messages before they are transmitted. When a user presses the PTT button, the audio is captured and stored in a buffer with an associated initiation time, ensuring that even if multiple users speak simultaneously, each user's audio is preserved in the buffer until it can be properly transmitted without loss.
2Ease of operation
If users press PTT button before connection establishment, then ease of operation is improved, but initial audio is missed
Solution Approach 1:
The system allows users to press the PTT button and begin speaking before connection is fully established. The audio is captured and buffered with an initiation time stamp during this pre-connection period. Once connection is established, the buffered audio is transmitted, ensuring no initial audio is lost while maintaining ease of operation.
3Loss of information
If audio messages are buffered and reordered based on importance, then information quality is improved, but device complexity increases
Solution Approach 1:
The system introduces an intermediary buffering mechanism that temporarily stores audio messages with their initiation times. This buffer acts as a mediator between audio capture and transmission, allowing messages to be reordered based on importance without requiring complex real-time processing. The buffer simplifies the overall system architecture by decoupling the capture and transmission phases.
4Loss of information
If AI engine is used to determine importance scores, then information prioritization is improved, but use of energy increases
Solution Approach 1:
The system applies local quality by using simple initiation time-based ordering for most audio messages, which requires minimal energy. The AI engine is selectively applied only to specific cases where importance differentiation is needed, rather than processing all audio messages uniformly. This localized application of AI reduces overall energy consumption while still achieving effective information prioritization when necessary.
Data Source
AI summary
A push-to-talk client device receives a request to initiate an audio message. The push-to-talk client device records a first part of the audio message for storage in a buffer. The push-to-talk client device stores the first part in the buffer. The push-to-talk client receives, from a push-to-talk server, a confirmation that the push-to-talk server is receiving audio of the audio message from the push-to-talk client device. The push-to-talk client device transmits, to the push-to-talk server, the recorded first part of the audio message from the buffer. The push-to-talk client device transmits, to the push-to-talk server, real-time audio of the audio message.