Noise Masking Device with Brain Activity Feedback
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Solution Overview
Problem
Existing noise masking systems struggle to automatically adjust masking sound volumes to effectively drown out unwanted noise without causing disturbance, as they lack feedback mechanisms to account for user sensitivity and changing noise levels.
Innovation Solution
A device with a transducer unit for noise detection, a sound generating unit, and a sensor unit for monitoring brain activity, which adjusts signal characteristics of the masking sound based on detected noise and user sensitivity, determined during a calibration process, to optimize noise masking while minimizing disturbance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If the masking sound volume is increased to drown out unwanted noise, then the noise masking effectiveness is improved, but the masking sound itself may become a source of disturbance
Solution Approach 1:
The system employs feedback mechanisms through brain activity sensors that continuously monitor the user's response to masking sounds. The controller adjusts the masking sound volume and characteristics in real-time based on the detected brain activity, ensuring the sound is loud enough to mask unwanted noise but not so loud as to cause disturbance. This closed-loop feedback system resolves the contradiction by dynamically balancing the two opposing requirements.
Solution Approach 2:
The system changes the parameters of the masking sound (volume, frequency, waveform) based on detected brain activity and noise levels. By dynamically adjusting these parameters rather than maintaining a fixed high volume, the system achieves effective noise masking while avoiding the generation of harmful disturbances. The controller modifies sound characteristics in response to real-time physiological feedback.
2Object-affected harmful factors
If the masking sound volume is manually adjusted by the user, then the balance between noise masking and disturbance avoidance is achieved, but the system lacks automation and requires user intervention
Solution Approach 1:
The system performs self-adjustment of masking sound parameters based on automatic detection of brain activity and noise levels. Rather than requiring manual user intervention, the controller autonomously monitors physiological signals and environmental noise, then automatically optimizes the masking sound characteristics. This self-service capability eliminates the need for user intervention while maintaining optimal balance between noise masking and disturbance avoidance.
Solution Approach 2:
The feedback loop enables automatic volume adjustment by continuously monitoring brain activity responses to masking sounds. The controller processes this feedback and autonomously adjusts sound parameters without user input. This automated feedback mechanism resolves the contradiction by providing both automation and optimal balance simultaneously.
3Extent of automation
If adaptive volume adjustment based on room noise level is implemented, then automatic volume adjustment is achieved, but the system does not account for user sensitivity to masking sounds
Solution Approach 1:
The system uses brain activity sensors to provide feedback on user sensitivity to masking sounds. This physiological feedback enables the controller to adapt the masking sound characteristics to individual user sensitivities, going beyond generic room noise-based adjustment. The feedback mechanism allows the system to personalize the audio output according to each user's neural response patterns.
Solution Approach 2:
The system changes masking sound parameters dynamically based on both room noise levels and detected user brain activity patterns. By incorporating multiple parameters (environmental noise and physiological response), the system achieves comprehensive adaptability to individual user sensitivities while maintaining automation. The controller integrates these multiple data streams to optimize sound characteristics for each user.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The device automatically adjusts masking sound characteristics to effectively mask external noise, reducing the risk of disturbance from the masking sound itself, and improving sleep quality by adapting to user sensitivity and noise patterns.
Implementation Method 1
a sensor unit for monitoring a user's brain activity
Implementation Method 2
a transducer unit for detecting noise to be masked
Implementation Method 3
a sound generating unit for generating a masking sound
Data Source
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Figure 3~5
AI summary
The invention provides a device (and method) for masking noise in which a calibration is carried out to determine the sensitivity of a user to a calibration sound. During use of the device, the signal characteristics of the masking sound are adjusted based on the detected noise, the response of the user to the detected noise and also the response of the user to the calibration sound. As a result, a masking sound is generated that is optimally adapted to mask unwanted noise, in particular in a way which avoids the masking noise itself becoming a disturbance to the particular user.