Audio-Based Window Position Control to Reduce Noise-Driven Wear
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automatic window control systems are limited by relying solely on meteorological information, leading to inaccuracies and a restricted number of automation contexts, which can result in unnecessary wear and annoyance due to frequent opening and closing, and do not account for specific household conditions.
Innovation Solution
A method that uses audio signal analysis and recognition to generate commands for actuator control, allowing for a broader range of automation contexts, including weather-related and nuisance-noise-related scenarios, and predicting audio signal variations to prevent annoying fluctuations in window position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If audio signal analysis is added to existing meteorological sensor systems, then the number of automation contexts is increased, but the device complexity increases
Solution Approach 1:
The control system is designed to process multiple types of signals (meteorological data from sensors and audio signals from microphones) through a unified command generation architecture. This allows the same control system to handle both weather-based automation and noise-based automation without requiring separate dedicated systems, thereby increasing adaptability while managing device complexity.
2Object-affected harmful factors
If the window automatically opens and closes in response to intermittent noise, then noise protection is improved, but wear on window components increases
Solution Approach 1:
The system performs preliminary analysis of audio signals to predict their duration and characteristics before triggering window movement. By anticipating the temporal profile of noise events, the system can avoid unnecessary opening/closing cycles for brief or non-persistent sounds, thereby reducing wear on mechanical components while maintaining adequate noise protection for significant disturbances.
Solution Approach 2:
The control system continuously monitors audio signal characteristics and adjusts its response based on the evolving noise pattern. This feedback mechanism allows the system to learn from repeated noise events and optimize its response threshold, reducing unnecessary actuations that would contribute to component wear while maintaining effective noise protection.
3Object-affected harmful factors
If frequent opening and closing is used to respond to varying noise levels, then noise protection is improved, but user annoyance increases
Solution Approach 1:
The system analyzes audio signal characteristics in advance, including predicted duration and intensity patterns, before triggering window movement. This preliminary assessment allows the system to filter out brief or insignificant noise events that would cause annoying frequent opening/closing, while still providing effective protection against persistent or intense noise sources.
Solution Approach 2:
The control system applies a threshold-based filtering mechanism that requires noise events to exceed certain duration and intensity criteria before triggering window response. This partial action approach prevents over-reaction to minor noise fluctuations, thereby reducing user annoyance from frequent unnecessary opening/closing while maintaining adequate noise protection for significant disturbances.
Data Source
AI summary
A method for automatic control of household-apparatus position, and in particular to fully opening, fully closing and partially opening, etc. French doors, roof windows (also called slanting skylights), French windows, doors, etc. the control method includes generating a command depending on an acquired audio signal, the generated command being able to trigger control by an actuator of a household apparatus, the actuator moving the household apparatus into a position that is dependent on the command. Thus, a number of automation contexts is increased and includes weather-unrelated contexts, for example nuisance-noise-related contexts, and, potentially, also weather-related contexts.