Earbud In-Ear Detection Using Feedback Microphone
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Solution Overview
Problem
Current in-ear detection methods for earbuds are inefficient in saving battery power and require dedicated devices, lacking effective means to differentiate between 'in-ear' and 'out-of-ear' states, especially in noisy conditions.
Innovation Solution
The use of a feedback microphone in earbuds to detect the 'in-ear' state by analyzing sound pressure levels at specific frequencies, comparing them to a reference out-of-ear spectrum, and evaluating criteria parameters to determine the earbud's position using discrete Fourier transforms and threshold values, allowing for compact and cost-effective in-ear detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated in-ear detection devices are used, then detection accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The feedback microphone, originally designed for noise cancellation purposes, is repurposed to perform in-ear detection functions as well. By analyzing the acoustic characteristics captured during normal operation, the system achieves dual functionality without adding dedicated detection hardware, thereby reducing device complexity while maintaining detection accuracy
Solution Approach 2:
The earbud system uses its own existing components (feedback microphone and speaker) to perform self-detection of insertion status. The feedback microphone captures acoustic signals that reveal whether the earbud is properly inserted, allowing the system to monitor its own state without external or dedicated detection devices
2Reliability
If continuous detection is performed, then detection reliability is improved, but battery consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system performs in-ear detection periodically by analyzing acoustic characteristics at specific intervals. The feedback microphone data is processed at discrete time points to determine insertion status, reducing computational load and power consumption while maintaining reliable detection through strategic sampling moments
Solution Approach 2:
The system performs detection analysis only when necessary or at optimized intervals rather than continuously. By processing feedback microphone data selectively based on operational conditions and using threshold-based decision making, the system achieves reliable detection with minimal computational effort and energy expenditure
3Device complexity
If simple detection methods are used, then device complexity is reduced, but detection precision deteriorates in noisy conditions
Solution Approach 1:
The system utilizes feedback from the noise cancellation microphone to inform the in-ear detection algorithm. By analyzing the acoustic characteristics already being captured for noise cancellation purposes, and comparing them against expected patterns, the system achieves robust detection in noisy environments without additional sensors or complex processing
Solution Approach 2:
The detection algorithm analyzes multiple acoustic parameters including frequency spectrum characteristics, sound pressure levels, and temporal patterns from the feedback microphone. By monitoring changes in these parameters over time and comparing them against reference profiles for inserted vs. non-inserted states, the system achieves accurate detection despite background noise interference
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
This method enables accurate in-ear detection, reducing battery consumption and eliminating the need for additional detection devices, while effectively distinguishing between 'in-ear' and 'out-of-ear' states even in noisy conditions, optimizing power management and user experience.
Implementation Method 1
analyzing sound pressure levels at specific frequencies
Implementation Method 2
evaluating criteria parameters to determine the earbud's position using discrete Fourier transforms
Data Source
AI summary
A method for in-ear detection, the method may include transmitting test signals, by a speaker of an earbud, during a test period, and while the earbud is operating at a first operational mode, wherein the test signals comprise at least one first test signal within a first frequency range, at least one second test signal within a second frequency range, and at one third test signal within a third frequency range; wherein the first frequency range, the second frequency range and the third frequency range differ from each other and are within a human auditory range; generating, by a feedback microphone of the earbud, sensed information that is indicative of audio signals sensed by the feedback microphone as a result of the transmitting of the test signals; and determining whether the earbud is located within an ear of a person, wherein the determining is based on the sensed information and a reference out of ear spectrum.