Headset Acoustic Dosimetry With Water Event Detection
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Solution Overview
Problem
Consumer electronic headsets lack effective long-term monitoring of sound exposure, which is crucial for preventing hearing health issues due to limited maximum sound output and the need to monitor sound levels over extended periods.
Innovation Solution
Implementing an acoustic dosimetry process that uses internal and external microphones in headsets and smart devices to measure in-ear sound pressure levels, integrate water and wind detectors for context, and aggregate sound exposure data from multiple devices to provide accurate and holistic sound exposure monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If acoustic dosimetry is implemented using only audio signals, then the monitoring function is simple, but measurement accuracy deteriorates due to water events causing false loud sound detections
Solution Approach 1:
A water event detector is introduced as an intermediary component that detects water events separately from audio signals. The processor uses this water event information as a mediator to filter or adjust audio-based sound exposure measurements, preventing false readings when water contacts the audio interface without requiring complex integration of multiple sensing modalities
Solution Approach 2:
The system merges water event detection capability with the existing acoustic dosimetry function. By combining the water event detector output with audio signal processing in the processor, the system achieves improved measurement accuracy through multi-parameter correlation while maintaining a unified, compact device structure rather than separate independent systems
2Measurement precision
If water event detection is added to improve measurement accuracy, then sound exposure measurement accuracy improves, but device complexity increases
Solution Approach 1:
The processor is designed to perform multiple functions: it processes audio signals for acoustic dosimetry, processes touch sensor images for water detection, and integrates both data streams to produce accurate sound exposure measurements. This multi-functionality eliminates the need for separate dedicated processing units, reducing overall device complexity while improving measurement accuracy
Solution Approach 2:
The system changes the parameter being measured from单纯的 audio signal intensity to a composite parameter that correlates audio levels with water event status. By monitoring changes in touch sensor image characteristics (water presence) alongside audio levels, the system achieves more accurate acoustic dosimetry without requiring complex additional hardware
3Measurement precision
If touch sensor images are processed continuously for water detection, then water event detection accuracy improves, but energy consumption increases
Solution Approach 1:
Instead of continuous processing, the system uses periodic action by only processing touch sensor images when water events are suspected or at scheduled intervals. The processor analyzes touch images to detect water events and uses this information to adjust audio monitoring, achieving accurate water detection while minimizing energy consumption through selective rather than continuous operation
Solution Approach 2:
The touch sensor subsystem serves dual purposes: it provides user interface functionality and simultaneously performs water detection for acoustic dosimetry correction. This self-service approach allows water event detection to occur using existing sensor infrastructure without requiring additional dedicated sensors or processing resources, thereby maintaining detection accuracy while controlling energy consumption
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 solution effectively monitors and reports sound exposure over time, providing users with insights into their hearing health risks, enabling them to take preventive measures against long-term hearing damage.
Implementation Method 1
measuring in-ear sound pressure level, SPL, that is caused by output audio being converted into sound by a headset
Implementation Method 2
A time sequence of touch images is acquired from a touch sensitive surface, and is processed in order to detect floating water on the touch sensitive surface
Data Source
AI summary
In-ear sound pressure level, SPL, is determined that is caused by output audio being converted into sound by a headset worn by a user. The in-ear SPL is converted into a sound sample having units that are suitable for evaluating sound noise exposure. These operations are repeated to produce a sequence of sound samples during playback. This sequence of sound samples is then written to a secure database. Access to the database is authorized by the user. Other aspects are also described and claimed.