Voice Headset Noise Cancellation with Dynamic Microphone Switching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing voice technologies fail to address the inefficiencies associated with voice navigation in industrial settings, particularly in the context of ambient noise interference, which hampers effective voice command interpretation and communication.
Innovation Solution
A method and system for a voice-detecting headset with a speech microphone and multiple noise-detecting microphones that dynamically activate and deactivate based on ambient noise levels, employing specific noise-cancellation algorithms to optimize voice detection and communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice navigation is used in ambient noise, then hands-free operation is enabled, but accuracy of voice command interpretation deteriorates
Solution Approach 1:
The system segments the microphone array into multiple independent noise-detecting microphones that can be selectively activated. Each microphone captures noise from different spatial directions, allowing the system to segment the noise cancellation task across multiple sensors and optimize which ones are active based on current noise conditions.
Solution Approach 2:
The system dynamically activates and deactivates noise-detecting microphones based on real-time ambient noise levels and characteristics. The processor monitors noise energy levels and adjusts microphone activation status accordingly, transitioning between different operational modes to optimize voice detection accuracy under varying noise conditions.
2Measurement precision
If multiple noise-detecting microphones are activated, then noise detection capability is improved, but power consumption increases
Solution Approach 1:
Instead of continuously activating all noise-detecting microphones, the system activates only the necessary subset based on current noise conditions. When noise levels are low or stable, fewer microphones remain active, reducing power consumption while maintaining sufficient noise detection capability through selective activation.
Solution Approach 2:
The system periodically monitors ambient noise levels and adjusts microphone activation in response to detected changes. This periodic assessment allows the system to transition between power-saving and high-performance modes based on actual environmental conditions, optimizing the balance between noise detection capability and power consumption.
3Measurement precision
If noise-cancellation algorithms are applied, then voice detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The system applies noise cancellation algorithms selectively based on the characteristics of detected noise. Different processing approaches are used depending on noise type (e.g., steady-state vs. transient noise) and environment, optimizing the balance between voice detection accuracy and processing complexity by matching algorithm complexity to noise severity.
Solution Approach 2:
The processor dynamically adjusts noise cancellation parameters based on detected noise energy levels and characteristics. When noise levels exceed thresholds, more aggressive cancellation parameters are applied; when noise is low, lighter processing is used, thereby adapting the processing complexity to the actual noise conditions rather than maintaining fixed high complexity.
Data Source
Figure 1
Figure 2A~2B
Figure 3~4
AI summary
Methods and systems provide dynamic selection of noise-cancelling algorithms, and dynamic activation and deactivation of microphones to provide multi-mode noise cancellation for a voice-detecting headset in situations where ambient noise prevents voice navigation from accurately interpreting voice commands. To do so, when an ambient noise is detected that exceeds a threshold, a particular noise-cancelling algorithm best-suited for the situation is selected, and one or more noise-detecting microphones is activated. The noise-detecting microphone(s) receiving the highest level of ambient noise can remain activated while the remaining noise-detecting microphones can be deactivated. A speech signal received by the speech microphone can then be optimized by cancelling the ambient noise signal received from the activated noise-detecting microphone(s) using the selected noise-cancelling algorithm. After the speech signal is optimized, it can be communicated to the voice-detecting headset for interpretation.