Dual-Processor Audio Classifier for Always-On Voice Recognition
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Solution Overview
Problem
Voice recognition systems and speaker authentication systems face significant processing and power consumption demands, making them challenging for mobile devices, especially when requiring continuous 'always-on' functionality, which can disrupt user experience and drain battery life.
Innovation Solution
An audio classifier is implemented using a dual-processor architecture with hard-wired logic for initial audio activity detection and reconfigurable logic for classification, allowing for efficient classification and activation of voice recognition systems without user intervention, reducing power consumption and processing overheads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice recognition systems operate in 'always-on' mode to improve user experience, then responsiveness and convenience are improved, but power consumption and processing demands increase significantly
Solution Approach 1:
The audio processing system is segmented into two distinct processors: a first processor with hard-wired logic for initial audio activity detection, and a second processor with reconfigurable logic for comprehensive audio classification. This segmentation allows the system to perform basic detection at low power while reserving higher-power processing for when needed, thereby maintaining always-on responsiveness without excessive power consumption.
Solution Approach 2:
The first processor performs partial audio analysis by detecting basic audio activity presence, which is sufficient for activation purposes. The second processor performs the remaining classification work only when the first processor detects activity. This partial action approach avoids the excessive power consumption of full always-on processing while maintaining system responsiveness.
2Speed
If voice recognition systems continuously monitor audio signals to enable always-on functionality, then system responsiveness is improved, but processing overheads increase
Solution Approach 1:
The processing workload is segmented between two processors with different capabilities. The first processor handles continuous monitoring with simple hard-wired logic for quick audio activity detection, providing fast responsiveness. The second processor handles complex classification only when needed, reducing overall processing overheads while maintaining system responsiveness.
Solution Approach 2:
The first processor acts as an intermediary between the continuous audio input and the second processor. It filters and pre-processes audio signals by detecting activity presence, thereby reducing the burden on the second processor and overall system processing overheads while maintaining fast response capability.
3Use of energy by moving object
If keyword detectors or button presses are required to activate voice recognition, then power consumption is reduced, but user convenience deteriorates
Solution Approach 1:
The audio classification system performs self-service by automatically detecting audio activity and classifying signals without requiring user intervention. The dual-processor system autonomously determines when voice commands are present and activates recognition accordingly, eliminating the need for keyword detection or button presses while maintaining low power consumption through selective processing.
Data Source
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AI summary
The disclosure relates to an audio classifier comprising: a first processor having hard-wired logic configured to receive an audio signal and detect audio activity from the audio signal, and a second processor having reconfgurable logic configured to classify the audio signal as a type of audio signal in response to the first processor detecting audio activity.