Beamforming Coefficients for Speech Recognition in Noisy Environments
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing voice-controlled devices face challenges in accurately recognizing speech in noisy home environments due to poor signal-to-noise ratios, as they lack effective methods to selectively focus on user speech while attenuating background noise.
Innovation Solution
The implementation of beamforming techniques that apply pre-calculated or on-demand beamformer coefficients to create beampatterns, which selectively focus on user speech by enhancing specific lobes of the audio signal, improving signal-to-noise ratios and enhancing processing resources for echo cancellation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beamforming techniques are applied to selectively focus on user speech, then speech recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating beamformer coefficients for multiple potential user positions before speech occurs. These coefficients are stored and ready for rapid application when speech is detected, avoiding real-time calculation complexity while maintaining beamforming precision for accurate speech recognition.
Solution Approach 2:
The system applies different beamforming coefficients to different spatial regions (lobes) based on where users are most likely to speak. By tailoring the beamforming characteristics to specific directions and positions, the system achieves high speech recognition accuracy in relevant areas without uniformly complex processing across all directions.
2Reliability
If processing resources are devoted to enhancing specific lobes of audio signal, then signal-to-noise ratio is improved, but computational load increases
Solution Approach 1:
The system applies enhanced processing only to specific lobes corresponding to directions where users are most likely to speak, rather than uniformly processing the entire audio spectrum. This localized approach improves signal-to-noise ratio for relevant speech while minimizing computational energy consumption in irrelevant directions.
Solution Approach 2:
The system applies beamforming enhancement to a subset of lobes that are most likely to contain user speech, rather than processing all possible directions equally. This partial action approach achieves sufficient signal-to-noise ratio improvement for practical speech recognition while avoiding the excessive computational load of omnidirectional processing.
3Speed
If pre-calculated beamformer coefficients are used, then processing speed is improved, but adaptability to different environments decreases
Solution Approach 1:
The system calculates and stores beamformer coefficients for multiple different environments and user positions in advance. These pre-calculated coefficient sets can be selected and applied based on the current environment, providing both fast processing speed through pre-computation and adaptability to different spatial configurations and acoustic conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach significantly improves the accuracy of speech recognition by selectively amplifying user speech and attenuating background noise, leading to better automatic speech recognition results and more efficient use of processing resources.
Implementation Method 1
Beamforming is the process of applying a set of beamformer coefficients to the signal data to create beampatterns, or effective directions of gain or attenuation
Implementation Method 2
these volumes may be considered to result from constructive and destructive interference between signals from individual microphones in a microphone array
Data Source
AI summary
Techniques for tailoring beamforming techniques to environments such that processing resources may be devoted to a portion of an audio signal corresponding to a lobe of a beampattern that is most likely to contain user speech. The techniques take into account both acoustic characteristics of an environment and heuristics regarding lobes that have previously been found to include user speech.