Videoconference Mute Control Using Intent-Aware Noise Gate Models
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Solution Overview
Problem
Current video conferencing systems face challenges in managing audio streams, as participants often forget to mute themselves, leading to distracting background noise and interruptions, while existing noise cancellation techniques are inadequate in filtering unwanted sounds like background conversations.
Innovation Solution
A computing system uses a gate control model to process communication data, including audio and visual signals, to predict a participant's intent to communicate, automatically controlling the mute function based on the predicted noise gate status, thereby filtering out extraneous audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual mute control is provided to participants, then participants can control their audio streams, but participants frequently forget to mute themselves leading to background noise
Solution Approach 1:
The system automatically detects and mutes background noise without requiring participant intervention. The noise cancellation algorithm continuously monitors audio streams and autonomously suppresses unwanted sounds, making the system self-regulating rather than relying on manual user actions.
Solution Approach 2:
The system provides real-time feedback to participants about their audio status and background noise levels. Visual indicators show when noise is detected and when muting is applied, creating a feedback loop that informs users of the system's actions and encourages proper usage.
2Object-generated harmful factors
If noise cancellation algorithms are applied, then background noise is suppressed, but unwanted speech such as background conversations are not effectively removed
Solution Approach 1:
The system dynamically adjusts noise cancellation parameters based on the detected audio characteristics. By analyzing speech patterns, frequency spectra, and temporal features, the algorithm adapts its filtering strength and type to distinguish between unwanted background noise and legitimate speech, improving accuracy across different scenarios.
Solution Approach 2:
Different noise cancellation strategies are applied to different frequency ranges and time periods. The system identifies specific frequency bands containing background noise and applies targeted filtering only to those bands, while preserving speech frequencies. The filtering intensity varies locally based on the detected audio environment.
3Productivity
If automatic mute control is implemented, then unnecessary audio transmissions are reduced, but system complexity increases
Solution Approach 1:
The patent replaces manual mechanical control (participants physically pressing mute buttons) with an automated electronic system using machine learning algorithms and signal processing. This substitution reduces the need for complex user interfaces and manual monitoring while improving efficiency through automatic noise detection and muting decisions.
Data Source
AI summary
Systems and methods of the present disclosure are directed to automatic control of mute controllers for participants in videoconferences. For example, a method for automatically controlling a mute control associated with a participant during a videoconference includes obtaining communication data associated with the participant participating in the videoconference. The communication data includes audio signals associated with the participant and/or visual signals associated with the participant. The method includes processing the communication data by a gate control model to generate an output. The output is indicative of an intent of the participant to communicate with other participants of the videoconference. The method includes generating a noise gate status based at least in part on the output associated with the gate control model. The method includes automatically controlling the mute control of the participant based at least in part on the noise gate status.