Beam Data Selection via Frequency Correlation for Wake-Up Detection
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Solution Overview
Problem
The high computing resource requirements and increased manufacturing costs of intelligent home devices due to parallel wake-up word detection across multiple beam data, which degrades user experience.
Innovation Solution
Selecting target beam data based on frequency sampling and correlation coefficients to prioritize processing on the most relevant data, reducing the need for comprehensive wake-up word detection across all beams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If parallel wake-up word detection is performed on all beam data, then wake-up detection speed is improved, but processing resource consumption increases and manufacturing cost increases
Solution Approach 1:
The patent segments the beam data processing into two stages: first performs wake-up detection on a small number of selected target beam data, then performs detection on remaining beam data only if needed. This segmentation reduces the parallel processing burden while maintaining detection speed by dividing the workload into priority-based batches.
Solution Approach 2:
The patent applies local quality by selecting specific target beam data with higher priority for wake-up detection based on beam quality metrics. Instead of uniform processing of all beams, it focuses computational resources on beams with better characteristics (higher signal-to-noise ratio, better correlation coefficients), thereby reducing overall processing requirements while maintaining detection effectiveness.
2Speed
If parallel wake-up word detection is performed on all beam data, then wake-up detection speed is improved, but manufacturing cost increases
Solution Approach 1:
The patent segments wake-up detection into priority-based batches, performing detection first on target beam data with higher correlation coefficients and signal quality. This segmentation allows the system to achieve effective wake-up detection with fewer parallel processing units, reducing hardware complexity and manufacturing cost while maintaining speed.
Solution Approach 2:
The patent changes the processing parameter by selectively applying wake-up detection to only the most promising beam data based on pre-calculated correlation coefficients and signal-to-noise ratios. This parameter-based selection reduces the number of parallel detection channels needed, thereby lowering manufacturing cost while preserving detection speed for critical cases.
3Device complexity
If target beam data is selected based on correlation coefficients, then processing resource demands are reduced, but detection accuracy may be affected
Solution Approach 1:
The patent performs preliminary actions by calculating correlation coefficients and signal-to-noise ratios for all beam data before wake-up detection. This preliminary assessment identifies target beam data with highest likelihood of containing wake-up words, allowing the system to focus processing resources on high-probability candidates and maintain detection accuracy while reducing overall resource demands.
Solution Approach 2:
The patent implements feedback by using correlation coefficients and signal quality metrics to guide the selection of target beam data. The system continuously evaluates beam characteristics and adjusts selection criteria based on observed performance, ensuring that processing resources are allocated to beams most likely to yield successful wake-up detection, thereby maintaining accuracy with reduced resources.
Data Source
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Figure 2b
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AI summary
The present invention relates to a method and device for selecting target beam data from a plurality of beams. The method includes: obtaining a plurality of beam data, and performing frequency sampling on each of the plurality of beam data; obtaining a plurality of beam frequency correlation coefficients based on frequency sampling data of each of the plurality of beam data, in which a beam frequency correlation coefficient is configured to indicate a similarity between one in the plurality of beam data and another one in the plurality of beam data; obtaining a beam frequency correlation coefficient sum corresponding to each of the plurality of beam data based on the plurality of beam frequency correlation coefficients; and selecting beam data having the beam frequency correlation coefficient sum satisfying a preset correlation coefficient requirement in the plurality of beam data as target beam data.