Keyword-Triggered Channel Selection by Maximum Power
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Solution Overview
Problem
Conventional keyword detection systems require significant computational resources and may incorrectly select multiple channels when the same keyword utterance is detected by multiple microphones, leading to inefficiencies.
Innovation Solution
A channel selection apparatus that combines input voice signals from multiple channels into a composite signal, calculates power levels, and selects the channel with the maximum power based on keyword detection results, optionally using candidate channel selection and weighted power calculations to reduce computational load and maintain signal-to-noise ratio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword detection processing is performed on all channels independently, then keyword detection accuracy is improved, but computation amount increases significantly
Solution Approach 1:
The system segments the keyword detection process into two stages: first performing detection on a composite signal formed by adding all channels, then selectively performing detection on individual channels only when the composite signal indicates a keyword utterance. This segmentation reduces the overall computational burden while maintaining detection accuracy.
Solution Approach 2:
The system merges all channel signals into a single composite signal for initial keyword detection. By combining the signals first and then detecting keywords, the system reduces the number of detection operations needed while still achieving accurate keyword detection across all channels.
2Reliability
If all channels detecting the keyword are selected, then keyword detection completeness is improved, but channel selection accuracy deteriorates when the same utterance is detected by multiple microphones
Solution Approach 1:
The system applies different selection criteria based on the detection results: when a keyword is detected in the composite signal, it selectively identifies only the specific channel(s) where the keyword actually occurs, rather than selecting all channels. This local quality approach ensures accurate channel identification while maintaining detection completeness.
Solution Approach 2:
The system replaces the mechanical approach of selecting all channels that detect a keyword with a signal processing approach that analyzes the composite signal characteristics to identify the specific source channel, thereby improving channel selection accuracy.
3Measurement precision
If multiple sets of keyword detection units are prepared for multiple channels, then channel-specific keyword detection is improved, but device complexity and cost increase
Solution Approach 1:
The system creates a universal keyword detection capability by processing a composite signal that contains information from all channels. This single detection mechanism serves the function of detecting keywords across all channels simultaneously, eliminating the need for multiple separate detection units while maintaining channel-specific detection capability.
Data Source
AI summary
A channel in which an utterance of a keyword is included is selected from acoustic signals of multiple channels. An addition unit 11 adds all channels of input voice signals of multiple channels to generate a composite voice signal of one channel. A keyword detection unit 12 generates a keyword detection result indicating a result of detecting an utterance of a predetermined keyword from a composite voice signal. A power calculation unit 13 calculates powers of channels based on input voice signals. A delay unit 14 delays the powers of the channels. When the keyword detection result indicates that the keyword was detected, a maximum power detection unit 15 selects, as an output channel, a channel having the maximum power among the powers of the channels of the input voice signals. A channel selection unit 16 selects the voice signal of the output channel from the input voice signals and outputs the selected voice signal.