Acoustic Event Control for Smart Devices Without Voice Wake-Up
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Solution Overview
Problem
Current voice interaction technologies require users to wake up a smart voice assistant and perform secondary interactions, leading to high computing workload and low control efficiency for smart devices, resulting in a poor user experience.
Innovation Solution
A method and apparatus that collect audio data to identify specific acoustic events, such as impulse signals, and execute corresponding control instructions without waking up the smart voice assistant, allowing direct device control based on detected events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wake-up interaction and instruction interaction are required to control smart devices, then voice interaction functionality is achieved, but computing workload increases and control efficiency decreases
Solution Approach 1:
The patent extracts and processes only impulse signal characteristics from audio data, separating this specific acoustic event detection from the full voice assistant wake-up process. By focusing solely on detecting impulse signals (clapping, knocking) and their acoustic event types, the system avoids the computational overhead of complete voice recognition while maintaining useful control functionality.
Solution Approach 2:
The audio data processing is segmented into specific acoustic event detection (impulse signals) rather than comprehensive voice recognition. The system divides the audio stream into target frames and identifies specific impulse patterns, allowing selective processing that reduces overall computational workload while preserving essential control capabilities.
2Measurement precision
If comprehensive voice recognition is implemented, then accurate instruction understanding is achieved, but processing time and computational resources increase
Solution Approach 1:
The system performs partial action by detecting only impulse signal acoustic events rather than conducting full voice recognition analysis. This selective approach processes only the necessary acoustic characteristics (impulse patterns, timing, intensity) to identify control-relevant events, achieving sufficient accuracy for device control without the time cost of comprehensive speech understanding.
Data Source
AI summary
A method for controlling a device includes: collecting audio data where the device is located; determining whether each target frame of the audio data is a first type signal; in response to the target frame of the audio data being the first type signal, determining an acoustic event type represented by the first type signal; and controlling the device to execute control instructions corresponding to the acoustic event type.